INOCS

INOCS - 2025

2025‌Activity reportProject-TeamINOCS

RNSR: 201521338H

Research center‌ Inria Centre at the University of Lille
In‌ partnership with:Ecole Centrale de Lille, Université Libre‌ de Bruxelles
Team name: INtegrated Optimization with Complex‌ Structure

Creation of the Project-Team: 2019 May 01‌

Each year, Inria research teams publish an Activity‌ Report presenting their work and results over the‌ reporting period. These reports follow a common structure,‌ with some optional sections depending on the specific‌ team. They typically begin by outlining the overall‌ objectives and research programme, including the main research‌ themes, goals, and methodological approaches. They also describe‌ the application domains targeted by the team, highlighting‌ the scientific or societal contexts in which their‌ work is situated.

The reports then present the‌ highlights of the year, covering major scientific achievements,‌ software developments, or teaching contributions. When relevant, they‌ include sections on software, platforms, and open data,‌ detailing the tools developed and how they are‌ shared. A substantial part is dedicated to new‌ results, where scientific contributions are described in detail,‌ often with subsections specifying participants and associated keywords.‌

Finally, the Activity Report addresses funding, contracts, partnerships,‌ and collaborations at various levels, from industrial agreements‌ to international cooperations. It also covers dissemination and‌ teaching activities, such as participation in scientific events,‌ outreach, and supervision. The document concludes with a‌ presentation of scientific production, including major publications and‌ those produced during the year.

Keywords

Computer Science‌ and Digital Science

A6. Modeling, simulation and control‌
A6.1. Methods in mathematical modeling
A6.2. Scientific computing,‌ Numerical Analysis & Optimization
A6.2.6. Optimization
A9. Artificial‌ intelligence
A9.6. Decision support

1 Team members,‌ visitors, external collaborators

Research‌‌ Scientists

Luce Brotcorne [Team leader, INRIA‌, Senior Researcher,‌ HDR]
Hélène Le‌‌ Cadre [INRIA, Researcher]
Jacques-Marius Roland‌ [INRIA, Researcher‌]

Faculty Members

Gaël‌‌ Guillot [CENTRALE LILLE, Associate Professor,‌ from Sep 2025]‌
Martine Labbé [ULB‌‌, Professor]
Maxime Ogier [CENTRALE LILLE‌, Associate Professor]‌
Frederic Semet [CENTRALE‌‌ LILLE, Professor, HDR]
Yue Su‌ [UNIV LILLE,‌ Associate Professor]

Post-Doctoral‌‌ Fellows

Sebastian Andres Davila Galvez [INRIA,‌ Post-Doctoral Fellow, until‌ Mar 2025]
Fredy‌‌ Vale Manuel Pokou [INRIA, Post-Doctoral Fellow‌, until Jul 2025‌]

PhD Students

Tifaout‌‌ Almeftah [INRIA, until Mar 2025]‌
Adrien Belfer [INRIA‌]
Nathan Davouse [‌‌INRIA, from Mar 2025]
Mathis Guckert‌ [INRIA]
Salma‌ Janati [INRIA]‌‌
Mesut Can Koseoglu [INRIA]
Francesco Morri‌ [INRIA, from‌ Oct 2025]
Luis‌‌ Antonio Rojo Gonzalez [UNIV CHILI]
Juan‌ Pablo Sepulveda Adriazola [‌INRIA, until Jan‌‌ 2025]
Wenjiao Sun [CENTRALE LILLE,‌ until Aug 2025]‌
Pablo Torrealba Gonzalez [‌‌CENTRALE LILLE, until Apr 2025]
Nathalia‌ Isabel Wolf Garcia [‌INRIA]
Zhenyu Wu‌‌ [INRIA]

Technical Staff

Barbara Cotrim Rodrigues‌ [INRIA, Engineer‌, from Mar 2025‌‌]
Gaël Guillot [INRIA, Engineer,‌ until Aug 2025]‌
Gloria Rebeca Murillo Cordova‌‌ [INRIA, Engineer, from Mar 2025‌]

Interns and Apprentices‌

Carlos Antil Catripay [‌‌INRIA, Intern, until Apr 2025]‌
Ricardo Ignacio Barriga Vera‌ [INRIA, Intern‌‌, until Apr 2025]
Enzo Thessieu [‌INRIA, Intern,‌ from Sep 2025]‌‌

Administrative Assistants

Nathalie Bonte [INRIA, until‌ May 2025]
Karine‌ Lewandowski [INRIA,‌‌ from Jun 2025]

Visiting Scientists

Pierre Gruet‌ [EDF]
Juan‌ Pablo Sepulveda Adriazola [‌‌UNIV LILLE]

2 Overall objectives

2.1 Introduction‌

INOCS is a cross-border‌ “France-Belgium” project team in‌‌ the Applied Mathematics Computation and Simulation Inria domain.‌ The main goal of‌ this team is the‌‌ study of optimization problems involving complex structures. The‌ scientific objectives of INOCS‌ are related to modeling‌‌ and methodological concerns. The INOCS team focuses on:‌

integrated models for problems‌ with Complex Structure (CS)‌‌ taking into account the whole structure of the‌ problem;
the development of‌ solution methods taking explicitly‌‌ into account the nature and the structure of‌ the decisions as well‌ as the properties of‌‌ the problem.

Even if CS problems are‌ in general NP-hard due‌ to their complex nature,‌‌ exact solution methods or matheuristics (heuristics based on‌ exact optimization methods) are‌ developed by INOCS. The‌‌ scientific contribution of INOCS will result in a‌ toolbox of models and‌ methods to solve challenging‌‌ real-life problems.

2.2 Schedule‌ of tasks

The research program development of INOCS‌ is to move alternatively:

from problems towards new‌ approaches in optimization: models and solution algorithms‌ will be developed to fit the structure and‌ properties of the problem; from them, new generic‌ approaches will be used to optimize problems with‌ similar properties;
from innovative approaches towards problems:‌ the relevance of the proposed approaches will be‌ assessed by designing new models and/or solution methods‌ for various classes of problems; these models and‌ methods will be based on the extension and‌ integration of specific, well-studied models and methods.

Even‌ if these two axes are developed sequentially in‌ a first phase, their interactions will lead us‌ to explore them jointly in the mid-term.

3‌ Research program

3.1 Introduction

An optimization problem consists‌ in finding a best solution from a set‌ of feasible solutions. Such a problem can be‌ typically modeled as a mathematical program in which‌ decision variables must (i) satisfy a set of‌ constraints that translate the feasibility of the solution‌ and (ii) optimize some (or several) objective function(s).‌ Optimization problems are usually classified into strategic,‌ tactical and operational problems, according to the types‌ of decisions to be taken.

We consider that‌ an optimization problem presents a Complex Structure (CS)‌ when it involves decisions of different types/nature (i.e.‌ strategic, tactical or operational) and/or presents some hierarchical‌ leader-follower structure. The set of constraints may usually‌ be partitioned into global constraints, linking variables‌ associated with the different types/nature of decision, and‌ constraints involving each type of variables separately.‌ Optimization problems with complex structure lead to extremely‌ challenging problems since a global optimum with respect‌ to the whole sets of decision variables and‌ of constraints must be determined.

Significant progress has‌ been made in optimization to solve academic problems.‌ Nowadays large-scale instances of some $N P$ -hard‌ problems are routinely solved to optimality. Our vision‌ within INOCS is to make the same advances‌ while addressing CS optimization problems. To achieve‌ this goal we aim to develop global solution‌ approaches at the opposite of the current trend.‌ INOCS team members have already proposed some successful‌ methods following this research lines to model and‌ solve CS problems (e.g. ANR project RESPET,‌ Brotcorne et al. 59, 60, Gendron‌ et al. 61, 62, 63,‌ and Strack et al. 64). However, these‌ are preliminary attempts and a number of challenges‌ regarding modeling and methodological issues have still to‌ be met.

3.2 Modeling problems with complex structures‌

A classical optimization problem can be formulated as‌ follows:

\begin{matrix} min & f (​​﻿﻿ x) \\ s .​​​‌ t . & x \in﻿​﻿﻿ X . \end{matrix}

In‌ this problem, $X$ is the set of feasible‌ solutions. Typically, in mathematical programming, $X$ is defined‌ by a set of constraints. $x$ may be‌ also limited to non-negative integer values.

The INOCS‌ team plans to address optimization problems where two types of decisions are‌ addressed jointly and are‌ interrelated. More precisely, let‌‌ us assume that variables $x$ and $y$ are‌ associated with these decisions.‌ A generic model for‌‌ CS problems is the following:

\begin{matrix} min & g (​​​‌ x, y)﻿﻿﻿‌ \\ s . t .﻿‌​‌ & x \in X,﻿​​﻿ \\ (x, y​​​‌) \in X Y﻿﻿﻿‌, \\ y \in Y﻿‌​‌ (x) .﻿​​﻿ \end{matrix}

In this model,‌ $X$ is the set‌ of feasible values for‌‌ $x$ . $X Y$ is the set of‌ feasible values for $x‌$ and $y$ jointly. This‌‌ set is typically modeled through linking constraints. Last,‌ $Y (x)‌$ is the set of‌‌ feasible values for $y$ for a given $x‌$ . In INOCS, we‌ do not assume that‌‌ $Y (x)$ has any properties.

The‌ INOCS team plans to‌ model optimization CS problems‌‌ according to three types of optimization paradigms: large‌ scale complex structures optimization,‌ bilevel optimization and robust/stochastic‌‌ optimization. These paradigms instantiate specific variants of the‌ generic model.

Large scale‌ complex structures optimization problems‌‌ can be formulated through the simplest variant of‌ the generic model given‌ above. In this case,‌‌ it is assumed that $Y (x)‌$ does not depend on‌ $x$ . In such‌‌ models, $X$ and $Y$ are associated with constraints‌ on $x$ and on‌ $y$ , $X Y‌‌$ are the linking constraints. $x$ and $y$ can‌ take continuous or integer‌ values. Note that all‌‌ the problem data are deterministically known.

Bilevel programs‌ allow the modeling of‌ situations in which a‌‌ decision-maker, hereafter the leader, optimizes his objective by‌ taking explicitly into account‌ the response of another‌‌ decision maker or set of decision makers (the‌ follower) to their decisions.‌ Bilevel programs are closely‌‌ related to Stackelberg (leader-follower) games as well as‌ to the principal-agent paradigm‌ in economics. In other‌‌ words, bilevel programs can be considered as demand-offer‌ equilibrium models where the‌ demand is the result‌‌ of another mathematical problem. Bilevel problems can be‌ formulated through the generic‌ CS model when $Y‌‌ (x)$ corresponds to the optimal solutions‌ of a mathematical program‌ defined for a given‌‌ $x$ , i.e. $Y (x) =‌ arg min {h (‌ x, y)‌‌ | y \in {Y}_{2}, (x‌, y) \in‌ X Y_{2}}$ where‌‌ $Y_{2}$ is defined by a set of‌ constraints on $y$ ,‌ and $X Y_{2‌‌}$ is associated with the linking constraints.

In robust/stochastic‌ optimization, it is assumed‌ that the data related‌‌ to a problem are subject to uncertainty. In‌ stochastic optimization, probability distributions‌ governing the data are‌‌ known, and the objective function involves mathematical expectation(s).‌ In robust optimization, uncertain‌ data take value within‌‌ specified sets, and the function to optimize is‌ formulated in terms of‌ a min-max objective typically‌‌ (the solution must be optimal for the worst-case‌ scenario). A standard modeling‌ of uncertainty on data‌‌ is obtained by defining‌ a set of possible scenarios that can be‌ described explicitly or implicitly. In stochastic optimization, in‌ addition, a probability of occurrence is associated with‌ each scenario and the expected objective value is‌ optimized.

3.3 Solving problems with complex structures

Standard‌ solution methods developed for CS problems solve independent‌ subproblems associated with each type of variables without‌ explicitly integrating their interactions or integrating them iteratively‌ in a heuristic way. However these subproblems are‌ intrinsically linked and should be addressed jointly. In‌ mathematicaloptimization a classical approach is to approximate‌ the convex hull of the integer solutions of‌ the model by its linear relaxation. The main‌ solution methods are (1) polyhedral solution methods which‌ strengthen this linear relaxation by adding valid inequalities,‌ (2) decomposition solution methods (Dantzig Wolfe, Lagrangian Relaxation,‌ Benders decomposition) which aim to obtain a better‌ approximation and solve it by generating extreme points/rays.‌ Main challenges are (1) the analysis of the‌ strength of the cuts and their separations for‌ polyhedral solution methods, (2) the decomposition schemes and‌ (3) the extreme points/rays generations for the decomposition‌ solution methods.

The main difficulty in solving bilevel‌ problems is due to their nonconvexity and nondifferentiability.‌ Even linear bilevel programs, where all functions involved‌ are affine, are computationally challenging despite their apparent‌ simplicity. Up to now, much research has been‌ devoted to bilevel problems with linear or convex‌ follower problems. In this case, the problem can‌ be reformulated as a single-level program involving complementarity‌ constraints, exemplifying the dual nature, continuous and combinatorial,‌ of bilevel programs.

4 Application domains

4.1 Energy‌

In energy, the team mainly focuses on pricing‌ models for demand side management, on bids definition‌ in the energy market and on the design‌ and pricing of electric car charging stations.

Demand‌ side management methods are traditionally used to control‌ electricity demand which became quite irregular recently and‌ resulted in supply inefficiency. We have explored the‌ relationship between energy suppliers and customers who are‌ connected to a smart grid. The smart grid‌ technology allows customers to keep track of hourly‌ prices and shift their demand accordingly, and allows‌ the provider to observe the actual demand response‌ to its pricing strategy. We tackle pricing problems‌ in energy according to the bilevel optimization approaches.‌ Some research works in this domain are supported‌ by bilateral grants with EDF.

The increasing number‌ of agents, with different characteristics interacting on the‌ energy market leads to the definition of new‌ types of bidding process. We have modeled this‌ problem as a bilevel one where the lower‌ level is the instance allocating the bids (the‌ ISO).

The proliferation of electric cars in cities‌ has lead to the challenging problem of designing‌ and pricing charging stations in order to smooth‌ the demand over time. We are modeling this‌ problem as a bilevel one where the lower‌ level represents the choice of users in a‌ preference list.

4.2 Transportation and logistics

In transportation and logistics, the team‌ addresses mainly integrated problems,‌ which require taking into‌‌ account simultaneously different types of decision. Examples are‌ location and routing, inventory‌ management and routing or‌‌ staff scheduling and warehouse operations management. Such problems‌ occur from the supply‌ chain design level to‌‌ the logistic facility level.

4.3 Telecommunications

In telecommunications,‌ the team mainly focuses‌ on network design problems‌‌ and on routing problems. Such problems are optimization‌ problems with complex structure,‌ since the optimization of‌‌ capacity installation and traffic flow routing have to‌ be addressed simultaneously.

5‌ Social and environmental responsibility‌‌

The research works developed in the INOCS team‌ have environmental and societal‌ impacts through the application‌‌ areas they target. At the environmental level, the‌ works on the optimization‌ of transportation systems aim‌‌ at reducing the impact of transportation on society.‌ The applied works in‌ energy aim at a‌‌ better use of the smart grid and the‌ optimization of electricity production‌ from renewable sources. At‌‌ the societal level, the research works take into‌ account musculoskeletal disorders in‌ the activity of employees‌‌ within a warehouse. Finally, in health, the works‌ conducted on group testing‌ allow the development of‌‌ effective campaigns of the population testing in preventive‌ medicine for example.

6‌ Highlights of the year‌‌

6.1 Awards

Yue Su won the following prizes‌ during the year 2025:‌
- 2025.06 Nominated as finalist‌‌ for the VeRoLog Doctoral Dissertation prize (accessible‌ ici)
- 2025.02 Second‌ Prize for "Best thesis‌‌ prize in transportation and logistics" from ROADEF (Association‌ Française de Recherche Opérationnelle‌ et d'Aide à la‌‌ Décision)
Ashok Krishnan K. S., Helene Le Cadre‌ , Ana Busic won‌ the Best Paper award‌‌ at the 12th International conference on network games,‌ control and optimization (NETGCOOP‌ 2025) for their paper‌‌ "Achieving a Collective Target Through Incentives" 24.‌

7 Latest software developments,‌ platforms, open data

7.1‌‌ Latest software developments

7.1.1 GroupTesting

Keywords:
Linear optimization,‌ Group Testing, Graph algorithmics‌
Functional Description:

Group testing‌‌ is a screening strategy that involves dividing a‌ population into several disjoint‌ groups of subjects. In‌‌ its simplest implementation, each group is tested with‌ a single test in‌ the first phase, while‌‌ in the second phase only subjects in positive‌ groups, if any, need‌ to be tested again‌‌ individually.

To contribute to the effort to tackle‌ the COVID-19 sanitary crisis,‌ we developed this software‌‌ which allows to create groups of individuals to‌ test via the group‌ testing technique while minimizing‌‌ a linear combination of the expected number of‌ false negative and false‌ positive classifications.

The test‌‌ design problem is modeled as a constrained shortest‌ path problem on a‌ specific graph and we‌‌ design and implement an ad hoc algorithm to‌ solve this problem. We‌ validate the algorithm on‌‌ instances based on Santé Publique France data on‌ COVID-19 screening tests.
Contact:‌
Frederic Semet

7.1.2 INOCSBox‌‌

Keywords:
Linear optimization, Operational research, Toolbox
Functional Description:‌

This software is a‌ toolbox that contains algorithms‌‌ that are frequently used‌ to solve optimization problems tackled by (but not‌ only) the team.

The objective of the toolbox‌ is to contain a set of code skeletons‌ that allow researchers to integrate adequate data structures‌ and basic algorithms for different structures complexity that‌ appears in the optimization problems we study. The‌ current version of the toolbox contains classical heuristic‌ tools (generic local search) to solve, among others,‌ the vehicle rouring problem and its variants. It‌ also contain a code to exactly and heuristically‌ solve the Shortest Path Problem with Ressource Constraints‌ that is usually encountered in the resolution of‌ problem with Branch-and-Price algorithms.

The future objective is‌ to include automatic reformulation tools for bi-level optimization‌ problems and state-of-the-art codes for the development of‌ decomposition methods.
Contact:
Tifaout Almeftah

7.1.3 SUMGtfsData

Name:‌
Parsing General Transit Feed Specification (GTFS) data for‌ optimization models.
Keywords:
Open data, Public transport
Functional‌ Description:
General Transit Feed Specification (GTFS) data into‌ GeoJSON format. To facilitate the integration of public‌ transportation data into geospatial analyses, visualizations, and scientific‌ models. In the context of SUM project, within‌ INRIA and INOCS team.
Contact:
Luce Brotcorne

7.1.4‌ SUMTariffSetting

Name:
Shared Urban Mobility Tariff setting software‌
Keywords:
Optimization, Public transport, Mobility
Functional Description:
This‌ tool provides a traveler-centric decision-support framework for integrated‌ public transport (PT) and new shared mobility (NSM),‌ combining pricing and trip recommendation in a bilevel‌ optimization model. It is designed to nudge travellers‌ to adopt sustainable multimodal options by offering curated‌ trip choices with dynamic pricing, while allowing the‌ PT operator to maximize multimodal ridership. The approach‌ includes stop aggregation via clustering to reduce problem‌ size, an exact single-level reformulation to solve the‌ bilevel model, and a matheuristic to improve scalability.‌
Contact:
Luce Brotcorne
Partner:
Delft University

7.1.5 SUMDesign&Routing‌

Name:
Shared Urban Mobility Joint design and routing‌ software
Keywords:
Optimization, Public transport, Mobility
Functional Description:‌
Implemented through an advanced optimization model (integer programming)‌ to design ideal service zones for bike-sharing systems,‌ incorporating multi-modal travel options and alternative routes to‌ boost user reach while staying within budget limits‌ for setup and daily operations. The model is‌ being experimented with public transport and shared bike‌ data from the city of Geneva, it delivers‌ optimized configurations of bike-sharing stations, their inventory, and‌ capacity over different time periods. The business value‌ centers on cutting costs and improving efficiency in‌ shared mobility, enabling cities and operators to offer‌ better-integrated transport services that attract more users and‌ therefore reduce reliance on private vehicles.
Contact:
Luce‌ Brotcorne
Partner:
Delft University

7.1.6 SUMImpactAssess

Name:
Shared‌ Urban Mobility Impact Assessment tools
Keywords:
Open data,‌ Impact, Mobility, Public transport, Web API
Functional Description:‌

The software is an API that executes data‌ analysis for impact assessments with data available in‌ the SUM Open Data Platform (ODP) https://odp.sum-project.eu.

The‌ API is developed in Python, and executes two‌ analysis, saving its results in the database :‌

1. ridge regression model for policy measures coefficients, based on living labs‌ KPIs variations

2. PROMETHEE-GAIA‌ for multi-criteria analysis, comparing‌‌ business activities (policy measures) from different perspectives

The‌ resulting metrics are then‌ saved in the database,‌‌ for the web client to display charts in‌ the SUM ODP website.‌ The SUM ODP is‌‌ an online platform focused on living labs activities‌ around shared mobility integrated‌ with public transport. It‌‌ allows to collect and monitor the policy measures‌ being implemented, and the‌ KPIs evaluating their impact.‌‌ The data is open to the public, allowing‌ visitors to view which‌ measures the living labs‌‌ implement and how the KPIs have evolved over‌ the 2-3 years of‌ the project. To provide‌‌ more value to this data, the platform also‌ proposes tools to assess‌ the impact of the‌‌ policy measures, so that decision makers can get‌ data driven recommendations.

The‌ platform is destined for‌‌ any user interested in New Shared Mobility integrated‌ with public transport :‌ cities, PT operators, shared-mobility‌‌ operators, MaaS providers, researchers, citizens, policy makers

Platform‌ available at : https://odp.sum-project.eu/‌
Contact:
Luce Brotcorne

7.1.7‌‌ SUModp

Name:
SUM Open Data Platform
Keywords:
Open‌ data, Web Application, Mobility,‌ Public transport
Functional Description:‌‌
The SUM ODP is an online platform focused‌ on living labs activities‌ around Urban Shared Mobility‌‌ integrated with public transport. It allows to collect‌ and monitor the policy‌ measures being implemented, and‌‌ the KPIs evaluating their impact. The data is‌ open to the public,‌ allowing visitors to view‌‌ which measures the living labs implement and how‌ the KPIs have evolved‌ over the 2-3 years‌‌ of the project. To provide more value to‌ this data, the platform‌ also proposes tools to‌‌ assess the impact of the policy measures, so‌ that decision makers can‌ get data driven recommendations.‌‌ The platform is destined for any user interested‌ in New Shared Mobility‌ integrated with public transport‌‌ : cities, PT operators, shared-mobility operators, MaaS providers,‌ researchers, citizens, policy makers‌ Platform available at :‌‌ https://odp.sum-project.eu/
URL:
https://odp.sum-project.eu/
Contact:
Luce Brotcorne

8 New‌ results

During the year‌ 2025, we addressed different‌‌ problems/challenges related to the three lines of research:‌ large scale complex structure‌ optimization, bilevel programming, game‌‌ theory and robust/stochastic programming. The main contributions are‌ summarized in the next‌ sections.

8.1 Large scale‌‌ complex structure optimization

Participants: Luce Brotcorne, Martine‌ Labbé, Maxime Ogier‌, Marius Roland,‌‌ Frédéric Semet.

8.1.1 The Branch-and-Bound Tree Closure‌

In 53, we‌ investigate the a-posteriori analysis‌‌ of Branch-and-Bound (BB) trees to extract structural information‌ about the feasible region‌ of mixed-binary linear programs.‌‌ We introduce three novel outer approximations of the‌ feasible region, systematically constructed‌ from a BB tree.‌‌ These are: a tight formulation based on disjunctive‌ programming, a branching-based formulation‌ derived from the tree's‌‌ branching logic, and a mixing-set formulation derived from‌ the on-off properties inside‌ the tree. We establish‌‌ an inclusion hierarchy, which ranks the approximations by‌ their theoretical strength w.r.t.‌ the original feasible region.‌‌ The analysis is extended‌ to the generation of valid inequalities, revealing a‌ separation-time hierarchy that mirrors the inclusion hierarchy in‌ reverse. This highlights a trade-off between the tightness‌ of an approximation and the computational cost of‌ generating cuts from it. Motivated by the computational‌ expense of the stronger approximations, we introduce a‌ new family of valid inequalities called star tree‌ inequalities. Although their closure forms the weakest of‌ the proposed approximations, their practical appeal lies in‌ an efficient, polynomial-time combinatorial separation algorithm. A computational‌ study on multi-dimensional knapsack and set-covering problems empirically‌ validates the theoretical findings. Moreover, these experiments confirm‌ that computationally useful valid inequalities can be generated‌ from BB trees obtained by solving optimization problems‌ considered in practice.

8.1.2 A tutorial on Branch-Price-and-Cut‌ algorithms

In 20, we propose a tutorial‌ on Branch-Price-and-Cut (BPC) algorithms for a generic class‌ of problems whose objective is to find a‌ set of feasible paths in a graph while‌ optimising a given objective function. The tutorial is‌ split into two main parts. First,we describe the‌ building blocks of a BPC algorithm: the Branch-and-Bound‌ algorithm, the column generation procedure and the Branch-and-Cut‌ algorithm. Then, we focus on the description of‌ a BPC algorithm for the class of problemswe‌ consider. Precisely,we present the classical and advanced techniques‌ that should be embedded in an efficient algorithm.‌ Particular attention is devoted to the solution of‌ the pricing problem in the case where it‌ is formulated as an Elementary Shortest Path Problem‌ with Resource Constraints. The aim of the tutorial‌ is pedagogical. Hence, its intended reader is someone‌ facing the first implementation of a BPC algorithm.‌ Implementation tips and examples accompany the techniques and‌ concepts to ease their comprehension. Precisely, the examples‌ are based on the Capacitated Vehicle Routing Problem,‌ which is a well-known problem belonging to the‌ class we consider.

8.1.3 Solving the Kidney Exchange‌ Problem

In 19, 33, we study‌ a Kidney Exchange Problem (KEP) with altruistic donors‌ and incompatible patient-donor pairs. Kidney exchanges can be‌ modelled in a directed graph as circuits starting‌ and ending in an incompatible pair or as‌ paths starting at an altruistic donor. For medical‌ reasons, circuits and paths are of limited length‌ and are associated with a medical benefit to‌ performing the transplants. The aim of the KEP‌ is to determine a set of disjoint kidney‌ exchanges of maximal medical benefit or maximal cardinality.‌ Several solution methods are available in the literature‌ to solve the KEP. However, they are usually‌ most able to efficiently address instances with specific‌ characteristics or of limited size. In this work,‌ we propose an efficient method that stands out‌ from the literature as it is able to‌ report solutions of very good quality on large-size‌ instances regardless of the characteristics (the type of‌ objective function and the maximum length of circuits‌ or paths). To do so, we consider a‌ set-packing formulation for the KEP with exponentially many variables associated with circuits‌ and paths, and develop‌ a Branch-Price-and-Cut algorithm to‌‌ solve it. As a methodological contribution, the BPC‌ algorithm features two novel‌ heuristics to separate a‌‌ well-known family of nonrobust inequalities, namely, the subset-row‌ inequalities. The heuristics aim‌ to detect such inequalities‌‌ via an original transformation from violated clique and‌ odd-hole inequalities. Extensive computational‌ experiments have been performed‌‌ on three sets of instances from the literature‌ and on a newly‌ generated set of challenging‌‌ instances. On the easiest instances, the BPC algorithm‌ yields results comparable with‌ the literature, whereas on‌‌ the other sets it clearly outperforms the previous‌ approaches.

8.1.4 Simultaneous vehicle‌ routing and driver scheduling‌‌ with flexible departure times

In 36, 37‌, 38, we‌ study the consideration of‌‌ flexible departure times in an integrated vehicle routing‌ and driver scheduling problem.‌ Integrated Vehicle Routing and‌‌ Driver Scheduling Problems (IVRDSPs) consist of simultaneously planning‌ routes for vehicles and‌ drivers' schedules. Unlike classical‌‌ routing problems, which usually assume each vehicle is‌ assigned to a single‌ driver, IVRDSPs allow a‌‌ single vehicle to be driven by different drivers‌ over time, and drivers‌ may change vehicles or‌‌ travel as passengers when needed. IVRDSPs find applications‌ in airlines, railways, and‌ buses, sharing common features‌‌ despite application-specific differences. A transportation task corresponds to‌ a travel from an‌ origin to a destination‌‌ with a predefined schedule, operated by one or‌ two combined vehicles, and‌ at least one driver.‌‌ Given a set of transportation tasks, the IVRDSP‌ aims to define driver‌ schedules and vehicle routes‌‌ in order to perform all tasks at minimum‌ cost. Usually, the IVRDSP‌ assumes the timing of‌‌ tasks to be fixed. However, this rigid assumption‌ can hinder some potential‌ connections between tasks because‌‌ of a few missing minutes to perform tasks‌ in sequence. This can‌ result in an increased‌‌ number of vehicles or drivers required to meet‌ the schedule and higher‌ operational costs. However, in‌‌ practice, task departure times can be slightly adjusted‌ in a given small‌ time window without any‌‌ problem. We model the IVRDSP with departure time‌ windows with a compact‌ Mixed-Integer Programming (MIP) formulation.‌‌ To solve large-scale instances in a reasonable amount‌ of time, we decompose‌ the problem into two‌‌ subproblems and propose a two-phase heuristic algorithm. First,‌ we solve the driver‌ scheduling subproblem using a‌‌ column generation approach. Then, given the resulting driver‌ schedules, we solve the‌ vehicle routing subproblem using‌‌ a compact MIP formulation. Computational results demonstrate the‌ efficiency of this approach,‌ confirming higher vehicle utilization,‌‌ as it allows feasible driver-vehicle pairing. Moreover, the‌ flexibility in departure times‌ can lead to solutions‌‌ with fewer drivers and/or vehicles, or less idle‌ time.

8.1.5 Agricultural Fleet‌ Vehicle Routing Problem with‌‌ Implements

The growing use of autonomous tractor fleets‌ with detachable implements presents‌ complex logistical challenges in‌‌ agriculture. Current systems often rely on simple heuristics‌ and avoid implement swapping,‌ limiting efficiency. A central‌‌ challenge is to dynamically‌ coordinate vehicle routing and implement exchanges to enable‌ efficient, low-intervention task execution. Due to high costs,‌ such fleets are owned primarily by large enterprises‌ or cooperatives, in which fair task allocation and‌ profit sharing are critical. We introduce the Agricultural‌ Fleet Vehicle Routing Problem with Implements (AFVRPI), addressing‌ both coordination and fairness. In 25, a‌ heuristic two-phase decomposition approach is described and its‌ efficiency is assessed. In 17, we propose‌ a distributed model derived from a centralized formulation‌ also presented in this paper. This model is‌ embedded within a Distributed Multi-Agent System Architecture (DIMASA),‌ where autonomous vehicle agents manage routing and implement‌ use under limited fuel autonomy, while implement agents‌ ensure compatibility and sufficient capacity to meet task‌ demands. Our solution applies systematic egalitarian social welfare‌ optimization to iteratively maximize the profit of the‌ worst-off vehicle, balancing fairness with system efficiency. To‌ enhance scalability, we use column generation in the‌ distributed model, achieving solution quality comparable to the‌ centralized model while significantly reducing computing time. Simulation‌ results on new benchmark instances demonstrate that our‌ distributed multi-agent AFVRPI approach is scalable, efficient, and‌ fair.

8.1.6 Order batching and picker routing problem‌ considering human factors

Manual picking in warehouses remains‌ a dominant. Typically, the IVRDSP assumes that task‌ timing is man capabilities allow for particularly flexible‌ and precise operations, but also pose significant ergonomic,‌ physical, and psychological challenges. Most traditional optimization models‌ totally neglect these human-centric issues, focusing primarily on‌ minimizing total time or distance. In 40,‌ we propose a novel modeling framework that integrates‌ picker discomfort into the picker routing decisions. The‌ proposed model is rather generic and can account‌ for multiple dimensions of human discomfort, including fatigue,‌ perceived exertion, and musculoskeletal disorders, under a single‌ notion of instantaneous discomfort, which dynamically evolves over‌ the course of a picking tour. Precisely, our‌ model assumes that each picking task is categorized‌ with a given level of complexity, based on‌ factors such as the item weight, the packaging,‌ the shelf height, and the accessibility of the‌ item. Performing a picking increases instantaneous discomfort depending‌ on the level of complexity of the task,‌ and the current discomfort level, resulting in non‌ linear phenomena where the accumulation of discomfort generates‌ more instantaneous discomfort. However, the model also allows‌ for recovery, i.e. a decrease in the discomfort,‌ when the picker is walking a reasonable distance‌ between two consecutive pick locations. To minimize the‌ total accumulated discomfort under such a non-linear discomfort‌ model, we propose a labeling algorithm that systematically‌ explores feasible picking sequences and evaluates discomfort dynamically.‌ Computational experiments on realistic warehouse layouts show that‌ the accumulated discomfort can be significantly reduced over‌ 40% on average with a limited (under 5%)‌ increase in total picking time. The results also‌ highlight the importance of sequencing tasks strategically and‌ exploiting opportunities to not saturate a picker with‌ several complicated tasks in a row and exploit recovery during travel.

In‌ warehouse operations, order picking‌ stands out as one‌‌ of the most critical processes, with its primary‌ objective being the preparation‌ of customer orders. In‌‌ 39, 54, we focus on a‌ classic picker-to-parts system, where‌ human pickers push a‌‌ trolley around the warehouse to collect different items.‌ From an operational point‌ of view, two main‌‌ decisions must be taken: order batching and picker‌ routing. The Order Batching‌ Problem (OBP) deals with‌‌ assigning customer orders to pickers, while the Picker‌ Routing Problem (PRP) consists‌ in determining, for a‌‌ single picker, the sequence in which the products‌ are collected. The integration‌ of both decisions defines‌‌ the Joint Order Batching and Picker Routing Problem‌ (JOBPRP), which remains challenging‌ to solve. While congestion‌‌ is a well-known phenomenon in warehouses with human‌ pickers, it is rarely‌ considered in optimization studies.‌‌ The existing literature mainly analyzes congestion through simulation,‌ often as an isolated‌ effect, whereas most works‌‌ addressing the JOBPRP assume ideal conditions without congestion,‌ despite its significant impact‌ on cost, performance, and‌‌ safety. In 54, we propose a rough‌ estimation of congestion levels‌ by introducing a time‌‌ discretization. In each time interval of the planning‌ horizon, an extra delay‌ is determined by using‌‌ a non-linear function based on the number of‌ pickers in each sub-aisle.‌ The delay is incurred‌‌ by all pickers in that sub-aisle. Based on‌ this model, we propose‌ in 39 an extended‌‌ Mixed Integer Programming formulation for the JOBPRP that‌ explicitly considers congestion aspects.‌ To solve the problem,‌‌ we propose a column generation-based heuristic based on‌ the resolution of the‌ root node. In each‌‌ iteration of the procedure, the pricing problem is‌ tackled using a dedicated‌ labeling algorithm. As a‌‌ main advantage, the labeling algorithm enables us to‌ address the main problem‌ complexities by precisely computing‌‌ the congestion value and discarding the exploration of‌ not relevant labels. Results‌ show the ability of‌‌ the algorithm to improve the initially provided solution‌ and reduce the initial‌ optimality gap.

8.1.7 Nuclear‌‌ Powerplants Scheduling with Uncertain Outage Durations

This project‌ in collaboration with EDF‌ R&D addresses the industrial‌‌ challenge of scheduling maintenance outages for nuclear power‌ plants. This task corresponds‌ to balancing the periodic‌‌ refueling with the condition to meet national energy‌ demand. The difficulty lies‌ in minimizing the reliance‌‌ on expensive and polluting non-nuclear energy sources while‌ satsifying the nuclear powerplants‌ operational constraints and unpredictable‌‌ delays in maintenance duration.

To tackle this issue‌ we developed a novel‌ computational framework that replaces‌‌ static planning with a dynamic, stochastic approach. A‌ first key achievement is‌ the implementation of a‌‌ Python-based transition graph generator. This software tool automatically‌ maps out every feasible‌ operational path for a‌‌ nuclear unit, creating a comprehensive map of potential‌ future states based on‌ specific unit parameters. Furthermore,‌‌ we successfully formulated a new optimization model that‌ explicitly handles uncertainty using‌ this synthetically generated data.‌‌ Unlike previous methods that‌ assumed fixed schedules, this new model adapts operational‌ decisions to a scenario based representation of outage‌ delays.

Looking ahead, this work provides the foundation‌ for a more robust national energy strategy. The‌ tools developed in this project allow for the‌ creation of "resilient" schedules that remain cost-effective even‌ when unexpected maintenance delays occur. Ultimately, this shift‌ towards stochastic optimization aims to significantly reduce the‌ economic risks associated with nuclear fleet management and‌ enhance the reliability of the national power supply.‌

8.1.8 Network Design of Bike Sharing Systems with‌ Public Transit Integration.

In 30, we focus‌ on the strategic design of service regions for‌ Bike-Sharing Systems (BSS) integrated with existing public transport‌ networks, addressing large-scale network design problems under budget‌ and operational constraints. The work investigates how multimodal‌ travel patterns, in particular PT-triggered first- and last-mile‌ demand, can be explicitly incorporated into the spatial‌ planning of bike-sharing infrastructure. We propose an integer‌ programming formulation that jointly determines station locations, capacities,‌ and initial inventories, while capturing multimodal accessibility through‌ a tailored set of $k$ -shortest multimodal paths.‌ To integrate operational considerations at the planning stage,‌ we introduce a distance-weighted rebalancing budget as a‌ tractable proxy for operational effort, reflecting the trade-off‌ between spatial dispersion and the resources required to‌ reposition bicycles. The proposed framework is evaluated through‌ extensive numerical experiments on synthesized demand scenarios and‌ a data-driven case study based on the central‌ Geneva area, combining GTFS public transport data with‌ an H3-based spatial discretization. The results illustrate how‌ demand heterogeneity and rebalancing constraints jointly shape station‌ deployment, capacity allocation, and system coverage, providing quantitative‌ insights for the design of cost-effective and operationally‌ feasible multimodal bike-sharing systems.

8.1.9 The Bi-objective Electric‌ Autonomous Dial-a-Ride Problem

The Electric Autonomous Dial-A-Ride Problem‌ (E-ADARP) introduces electric, autonomously driving vehicles and their‌ unique requirements into the classic dial-a-ride problem, where‌ people are transported between pickup and drop-off locations.‌ Next to an electric autonomous vehicle fleet, in‌ the literature, a weighted-sum objective function, which combines‌ the classic routing cost-oriented objective with a user-oriented‌ objective function, has usually been considered. The user-oriented‌ objective function minimizes the total excess user ride‌ time. In this work, we treat them as‌ two separate objective functions, which are optimized concurrently.‌ In order to address the resulting bi-objective E-ADARP,‌ 35 develop a novel exact framework (called fragment-based‌ checker), whose core part is a smart “select-and-check"‌ algorithm that iteratively constructs feasible solutions using fragments.‌ Several enhancements are proposed to enforce the computational‌ efficiency of the proposed method. In the computational‌ experiments, we evaluate several variants of our checker‌ algorithm by leveraging a previously developed branch-and-price algorithm.‌ We benchmark the checker-based framework against state-of-the-art criterion‌ space frameworks (e.g., the epsilon-constraint method and the‌ balanced-box method), as well as a generalized branch-and-price‌ algorithm. Numerical results on both bi-objective DARP and‌ E-ADARP instances demonstrate the effectiveness of the proposed‌ framework. With our proposed approaches, 21 out of 38 instances are solved‌ optimally, where small-to-medium-sized instances‌ are solved within seconds.‌‌ On larger-scale instances, especially those requiring high battery‌ end levels are computationally‌ challenging to solve, our‌‌ approaches provide high-quality approximations of the Pareto frontiers.‌ Efficient solutions with varying‌ energy restrictions are compared‌‌ and we obtain valuable managerial insights for different‌ kinds of service providers.‌

8.2 Bilevel programming and‌‌ game theory

Participants: Luce Brotcorne, Gaël Guillot‌, Martine Labbé,‌ Hélène Le Cadre,‌‌ Frédéric Semet.

8.2.1 Network games, artificial intelligence,‌ control and optimization

In‌ 41, Helene Le‌‌ Cadre , Yezekael Hayel, Bruno Tuffin, Tijani Chahed‌ edited a book that‌ constitutes the proceedings of‌‌ the 11-th International conference on network, control and‌ optimization 2024, NETGCOOP 2024,‌ held during October 9-11,‌‌ 2024, in Lille, France. The 13 full papers‌ and 1 short paper‌ were carefully reviewed and‌‌ selected from 21 submissions. The papers are organized‌ in the following sections:‌ scheduling, queuing systems and‌‌ resource allocation, modeling and performance, pricing and economics‌ models, energy, generative artificial‌ intelligence.

8.2.2 Noncooperative games‌‌ with prospect theoretic preferences

In 14, we‌ study noncooperative games with‌ uncertain payoffs, where agents‌‌ display irrational behaviors in response to underlying risk‌ factors. Our formulation incorporates‌ prospect theory, a behavioral‌‌ model used to describe agents' risk attitude, into‌ the equilibrium theory of‌ noncooperative N-agent games. We‌‌ show that the resulting nonconvex nonsmooth game admits‌ equilibria and provide convergence‌ guarantees for their computation.‌‌ Further, the concept of "Price of Irrationality" is‌ introduced to quantify the‌ suboptimality induced by irrational‌‌ behaviors. Finally, we provide bounds on the performance‌ of a class of‌ prospect theoretic aggregative games‌‌ and illustrate our results on an electricity market‌ game involving strategic end‌ users exposed to risk.‌‌

8.2.3 How irrationality shapes Nash equilibria: A prospect-theoretic‌ perspective

Noncooperative games with‌ uncertain payoffs have been‌‌ classically studied under the expected-utility theory framework, which‌ relies on the strong‌ assumption that agents behave‌‌ rationally. However, simple experiments on human decision makers‌ found them to be‌ not fully rational, due‌‌ to their subjective risk perception. Prospect theory was‌ proposed as an empirically-grounded‌ model to incorporate irrational‌‌ behaviours into game-theoretic models. But, how prospect theory‌ shapes the set of‌ Nash equilibria when considering‌‌ irrational agents, is still poorly understood. To this‌ end, we study in‌ 23 how prospect theoretic‌‌ transformations may generate new equilibria while eliminating existing‌ ones. Focusing on aggregative‌ games, we show that‌‌ capturing users' irrationality can preserve symmetric equilibria while‌ causing the vanishing of‌ asymmetric equilibria. Further, there‌‌ exist value functions which map uncountable sets of‌ equilibria in the expected-utility‌ maximization framework to finite‌‌ sets. This last result may shape some equilibrium‌ selection theories for human-in-the-loop‌ systems where computing a‌‌ single equilibrium is insufficient and comparison of equilibria‌ is needed.

8.2.4 Learning‌ market equilibria preserving statistical‌‌ privacy using performative prediction

In 16, we‌ consider a peer-to-peer electricity‌ market modeled as a‌‌ private network game, where‌ end users minimize their cost by computing their‌ demand and controllable generation. Their nominal demand constitutes‌ sensitive information that they might want to keep‌ private. We prove that the private network game‌ admits a unique variational equilibrium, which depends on‌ the private information of all end users. Thus,‌ to update their strategy, end users rely on‌ randomized readings. A data aggregator is introduced, which‌ aims to learn the end users’ private information,‌ while remunerating them depending on the quality of‌ their readings. Using performative prediction, we define a‌ decision-dependent game explicitly taking into account the distribution‌ shift caused by the end users’ hidden ability.‌ The decision-dependent game coincides with a Stackelberg game‌ when the end users’ hidden abilities are best‌ responses. Further, the market robustness can be quantified‌ by evaluating the efficiency loss as the difference‌ between the social cost in the performatively stable‌ equilibrium and the optimum. We show that under‌ mild assumptions, the performatively stable equilibrium can be‌ found by distributed and sequential variants of the‌ repeated stochastic gradient method while we propose a‌ two-timescale stochastic approximation method to learn Stackelberg equilibrium.‌ Finally, we formulate the data aggregator’s optimal contract‌ design as a bilevel optimization problem that we‌ cast as a more tractable nonlinear nonconvex optimization‌ problem which can be solved using simulated annealing.‌ Simulations on small and large scale problem instances‌ illustrate the results.

8.2.5 Achieving a collective target‌ through incentives

In many games, the payoff of‌ individual users is a function of a collective‌ outcome that is common to all agents. Often,‌ the users may be interested in jointly steering‌ this outcome to a desired value. In 24‌, we present such a scenario, where the‌ collective outcome is strictly monotone in the joint‌ strategy of the agents. Further, the agents are‌ irrational in their perception of a random cost‌ component in their payoff. This irrationality is modelled‌ using prospect theory. A coordinator steers the game‌ to a desired collective outcome, by designing incentives.‌ These incentives modify the responses of the users.‌ Owing to the potential structure of the game,‌ the system converges to a Nash equilibrium at‌ which the desired collective outcome is obtained.

8.2.6‌ A generalized potential game approach of UAV swarm‌ coordination for hidden target localization

In 15,‌ we consider a swarm of Unmanned Aerial Vehicles‌ (UAVs) carrying sensors with nondeterministic detection and noisy‌ localization measurements, while sharing observations with neighboring UAVs,‌ we address the problem of localization of a‌ hidden target in continuous space and discrete time.‌ The goal is to coordinate UAVs to maximize‌ the information gathered while minimizing their individual energy‌ costs. We formulate the problem as a time-varying‌ non-cooperative game with coupling constraints. We show that‌ the target is localized in finite time with‌ probability one and that the game has a‌ generalized potential structure. Further, we provide an exact‌ best-response algorithm for UAVs to iteratively compute their trajectories. Finally, we numerically‌ compare the potential game‌ to the team-based approach,‌‌ demonstrating comparable performance under different communication graph structures‌ and assessing the impact‌ of the swarm size‌‌ on various metrics.

8.2.7 Optimal electric vehicle charging‌ with dynamic pricing, customer‌ preferences and power peak‌‌ reduction

In this work 9, we consider‌ a provider of electric‌ vehicle charging stations that‌‌ operates a network of charging stations and use‌ time varying pricing to‌ maximize profit and reduce‌‌ the impact on the electric grid. We propose‌ a bilevel model with‌ a single leader and‌‌ multiple disjoint followers. The customers (followers) make decisions‌ independently from each other.‌ The provider (leader) sets‌‌ the price of charging for each station at‌ each time slot, and‌ ensures there is enough‌‌ energy to charge. The charging choice of each‌ customer is represented by‌ a combination of a‌‌ preference list of (station, time) pairs and a‌ reserve price. The proposed‌ model thus accounts for‌‌ the heterogeneity of customers with respect to price‌ sensitivity and charging preferences.‌ We define a single-level‌‌ reformulation based on a reformulation approach from the‌ literature on product line‌ optimization, and we report‌‌ computational results that highlight the efficiency of the‌ new reformulation and the‌ potential impact of our‌‌ approach for reducing peaks on the electricity grid.‌

8.2.8 Learning in conjectural‌ Stackelberg games

In 32‌‌, 27, we extend the formalism of‌ Conjectural Variations games to‌ Stackelberg games involving multiple‌‌ leaders and a single follower. To solve these‌ nonconvex games, a common‌ assumption is that the‌‌ leaders compute their strategies having perfect knowledge of‌ the follower's best response.‌ However, in practice, the‌‌ leaders may have little to no knowledge about‌ the other players' reactions.‌ To deal with this‌‌ lack of knowledge, we assume that each leader‌ can form conjectures about‌ the other players' best‌‌ responses, and update its strategy relying on these‌ conjectures. Our contributions are‌ twofold: (i) On the‌‌ theoretical side, we introduce the concept of Conjectural‌ Stackelberg Equilibrium -keeping our‌ formalism conjecture agnosticwith Stackelberg‌‌ Equilibrium being a refinement of it. (ii) On‌ the algorithmic side, we‌ introduce a two-stage algorithm‌‌ with guarantees of convergence, which allows the leaders‌ to first learn conjectures‌ on a training data‌‌ set, and then update their strategies. Theoretical results‌ are illustrated numerically.

8.2.9‌ Steering noncooperative games through‌‌ conjecture designs

In dynamic noncooperative games, each player‌ makes conjectures about other‌ players' reactions before choosing‌‌ a strategy. However, resulting equilibria may be multiple‌ and do not always‌ lead to desirable outcomes.‌‌ These issues are typically addressed separately -for example,‌ through opponent modeling and‌ incentive design. Drawing inspiration‌‌ from conjectural variations games, we propose in 51‌ an incentive design framework‌ in which a coordinator‌‌ first computes an equilibrium by optimizing a predefined‌ objective function, then communicates‌ this equilibrium as a‌‌ target for the players to reach. In a‌ centralized setting, the coordinator‌ also optimizes the conjectures‌‌ to steer the players‌ towards the target. In decentralized settings, players independently‌ compute conjectures and update their strategies based on‌ individual targets. We provide a guarantee of equilibrium‌ existence in both cases. This framework uses conjectures‌ not only to guide the system towards desirable‌ outcomes but also to decouple the game into‌ independent optimization problems, enabling efficient computation and parallelization‌ in large-scale settings. We illustrate our theoretical results‌ on classical representative noncooperative games, demonstrating its application‌ potential.

8.2.10 Pricing framework for cloud sharing

Cloud‌ sharing incentives and energy aware scheduling. In 55‌, we propose a dynamic pricing and incentive‌ framework for cloud computing that reallocates idle reserved‌ virtual machines from long term subscribers to short‌ term users in order to improve utilization and‌ reduce energy waste. The model is formulated as‌ a bilevel program in which the provider sets‌ dynamic rental prices and rewards, while subscribers decide‌ whether to share capacity and users decide when‌ to rent under time varying energy costs. In‌ our second work, we address energy aware scheduling‌ of large scale deep learning training on geo‌ distributed GPU data centers by combining a mixed‌ integer linear model with a rolling horizon scheme‌ and a column generation based matheuristic, enabling renewable‌ aware planning decisions at scale. Both works share‌ the objective of reducing the energy footprint of‌ digital infrastructures through optimization models that capture realistic‌ operational constraints and time varying energy conditions.

8.2.11‌ Spot fare inspection in proof of payment transit‌ systems

Spot fare inspection in proof of payment‌ transit systems. We study the operational implementation of‌ spot fare inspection in proof of payment urban‌ bus systems, where opportunistic passengers may evade payment‌ by selecting the most convenient path. The interaction‌ between the transit authority and passengers is modeled‌ as a leader follower Stackelberg game in which‌ the authority sets inspection frequencies over the network‌ and passengers best respond through their path choices.‌ We then address the practical deployment of these‌ frequencies by building an unpredictable allocation schedule whose‌ repeated day to day use matches the target‌ spot strategy. The resulting schedule construction is handled‌ with a column generation procedure, and two operational‌ inspection variants are analyzed, including inspections that interrupt‌ the bus service and inspections performed while the‌ bus is in motion.

8.2.12 Pricing and Trip‌ Recommendation Problem for integrated shared mobility systems

For‌ seamless shared urban mobility, traveler-centric mobility management is‌ crucial, particularly when integrating public transportation with new‌ shared-mobility services, which requires coordinated tactical–operational decisions. In‌ 22, we propose a hierarchical optimization framework‌ for the Capacitated Pricing and Trip Recommendation Problem‌ (CPTRP) that promotes PT-only and intermodal PT–bike-sharing (BSS)‌ usage with the aim of reducing car-based travel.‌ We formulate CPTRP as a bilevel optimization model‌ in which the PT operator (leader) selects BSS-leg‌ prices and curates a menu of PT-only and‌ intermodal trip alternatives, anticipating how users (followers) respond‌ through a deterministic generalized-disutility choice model under docking-station capacity and inventory feasibility‌ constraints. We derive an‌ exact single-level MILP reformulation‌‌ of the bilevel model. We benchmark the proposed‌ bilevel policies against a‌ reference setting with fixed‌‌ BSS prices and no trip recommendation to quantify‌ the incremental value of‌ the interventions. We evaluate‌‌ the framework on a case study built from‌ Geneva’s PT–BSS network using‌ real trip alternatives.

In‌‌ 29, we introduce a matheuristic to obtain‌ high-quality solutions for larger‌ instances within practical runtimes‌‌ for CPTRP framework. We evaluate both exact single-level‌ MILP reformulation of the‌ bilevel model and proposed‌‌ matheuristic algorithm on a case study built from‌ Geneva’s PT–BSS network using‌ real trip alternatives with‌‌ instances covering nearly the whole city network. Relative‌ to a reference setting‌ with fixed BSS prices‌‌ and no trip recommendation, the proposed approach reduces‌ car-trip demand by 25.2%‌ on average across instances,‌‌ increases shared-mobility market share by up to 26.53%‌, and yields intermodal‌ PT–BSS shares of up‌‌ to 33.61% of total demand. The matheuristic achieves‌ scalable computation. It achieves‌ a median optimality gap‌‌ of 8.6% while reducing solution time by a‌ median 79.32%, and‌ it yields high-quality solutions‌‌ for instances where a commercial MILP solver applied‌ to the reformulation does‌ not converge within the‌‌ imposed time limit. Overall, the framework provides actionable‌ decision support at the‌ tactical–operational level by quantifying‌‌ trade-offs among car-trip reduction, ridership, operating profit, and‌ station feasibility when deploying‌ pricing and recommendation policies.‌‌

8.2.13 Game theory and multi-agent reinforcement learning for‌ zonal ancillary markets

We‌ characterize zonal ancillary market‌‌ coupling relying on noncooperative game theory in 48‌. To that purpose,‌ we formulate the ancillary‌‌ market as a multi-leader single follower bilevel problem,‌ that we subsequently cast‌ as a generalized Nash‌‌ game with side constraints and nonconvex feasibility sets.‌ We determine conditions for‌ equilibrium existence and show‌‌ that the game has a generalized potential game‌ structure. To compute market‌ equilibrium, we rely on‌‌ two exact approaches: an integrated optimization approach and‌ Gauss-Seidel best-response, that we‌ compare against multi-agent deep‌‌ reinforcement learning. On real data from Germany and‌ Austria, simulations indicate that‌ multi-agent deep reinforcement learning‌‌ achieves the smallest convergence rate but requires pretraining,‌ while bestresponse is the‌ slowest. On the economics‌‌ side, multi-agent deep reinforcement learning results in smaller‌ market costs compared to‌ the exact methods, but‌‌ at the cost of higher variability in the‌ profit allocation among stakeholders.‌ Further, stronger coupling between‌‌ zones tends to reduce costs for larger zones.‌

8.2.14 Bilevel programming for‌ combinatorial coalition formation

Companies‌‌ frequently offer wholesale prices for their products that‌ decrease with the number‌ of purchased items. However,‌‌ single buyers may not be willing or able‌ to purchase large quantities‌ of a single item.‌‌ Nevertheless, consumers can form groups to purchase at‌ wholesale prices, obtaining bargaining‌ power. This practice can‌‌ be extended from single products to bundles. In‌ 12, we propose‌ a mathematical model to‌‌ create groups of buyers‌ wishing to purchase product bundles optimally. Mixed-integer programming‌ formulations are presented for the cases of non-increasing‌ price and step price functions. A Benders decomposition‌ formulation is proposed for step price functions to‌ solve large instances. Computational experiments show the performance‌ of the method for synthetic instances.

8.2.15 Stackelberg‌ security games

Anticipating the behavior of potential attackers‌ is essential for protecting critical infrastructure, a challenge‌ that can be modeled as a Stackelberg security‌ game. In this setting, a defender allocates limited‌ resources to protect targets so as to maximize‌ expected utility, while anticipating that attackers will respond‌ optimally. In 11, we introduce novel valid‌ inequalities to compute Strong Stackelberg Equilibria in both‌ general Stackelberg games and Stackelberg security games. We‌ also study a budget-constrained security game and propose‌ a branch-and-price solution approach. Computational results show that‌ the proposed formulation significantly outperforms standard models from‌ the literature in terms of solution time and‌ memory consumption. In addition, an extensive sensitivity analysis‌ evaluates key branch-and-price parameters, including column initialization, column‌ generation per iteration, and stabilization techniques. Overall, our‌ method reduces solution times to less than one‌ fifth of those required by state-of-the-art approaches.

8.3‌ Robust/stochastic programming

Participants: Luce Brotcorne, Martine Labbé‌, Hélène Le Cadre, Marius Roland,‌ Yue Su.

8.3.1 On Multidimensonal Disjunctive Inequalities‌ for Chance-Constrained Stochastic Problems with Finite Support

In‌ 46, we study mixed-integer linear Chance-Constrained problems‌ (CCPs) characterized by random vectors with finite support.‌ The primary objective of our work is to‌ enhance branch-and-bound or branch-and-cut approaches through the introduction‌ of new valid inequalities. We propose two novel‌ families of inequalities: primal-dual valid inequalities (PD-VIs), inspired‌ by the quantile definition of chance constraints, and‌ covering valid inequalities (C-VIs). By re-scaling the coefficients‌ of the C-VIs, we derive multi-disjunctive valid inequalities‌ (MD-VIs), which serve as a generalization of the‌ simple-disjunctive valid inequalities previously established in the literature.‌ Theoretical analysis demonstrated that C-VIs dominate PD-VIs, while‌ also establishing that the separation problem for C-VIs‌ is NP-hard. Consequently, we develop heuristic separation procedures,‌ including an Alternating Direction Method (ADM)-like approach, to‌ efficiently identify violated cuts. Furthermore, the research provided‌ theoretical results regarding the closure properties of MD-VIs‌ with respect to the closure of quantile cuts.‌ Extensive numerical experiments conducted on covering-type k-violation linear‌ programs and chance-constrained multidimensional knapsack problems validated the‌ approach, showing that MD-VIs yield significant improvements in‌ dual bounds and outperform state-of-the-art inequalities, such as‌ mixing-set inequalities and quantile cuts, in a branch-and-cut‌ framework.

8.3.2 Decision Focused Learning for Dynamic Pricing‌ and Inventory Control of Substitutable Products

In 34‌, 28, we study the dynamic pricing‌ for substitutable itineraries presents in airline revenue management,‌ which poses significant challenges due to the stochastic‌ nature of customer choices. The dynamic pricing problem‌ can be conceptualized as a Markov Decision Process‌ with the objective of maximizing the total expected‌ revenue over a finite selling horizon. However, the complexity of this model,‌ characterized by its multi-dimensional‌ state and action spaces,‌‌ makes it computationally prohibitive to solve exactly, even‌ for small-to-medium-sized instances. As‌ a result, classic methods‌‌ such as dynamic programming (DP) prove inefficient in‌ addressing the dynamic pricing‌ problem for larger instances.‌‌ In this work, we introduce learning-based policies tailored‌ to the dynamic pricing‌ problem of substitute itineraries.‌‌ We proposed two different machine learning (ML) pipelines‌ to encode pricing policy‌ and designed tailored training‌‌ processes for each of them. Compared to existing‌ methods, our approach has‌ two advantages:

Benchmarking the‌‌ proposed policies against the optimal pricing policy, we‌ show that learning-based policies‌ seem to be a‌‌ good approximation for the optimal policy.
Our learning-based‌ policies can lead to‌ huge speed-up in solving‌‌ large-scale instances (with up to million-magnitude state space)‌ by moving all CPU‌ time offline.

Our pipeline-encoded‌‌ polcies are benchmarked with industrial practice implemented in‌ Air France, which leads‌ to revenue enhancement with‌‌ up to 74%.

8.3.3 Stochastic Bilevel Optimization

We‌ study stochastic mixed integer‌ bilevel linear problems in‌‌ which the leader commits before uncertainty is revealed‌ and the follower reacts‌ after observing the realization,‌‌ with uncertainty entering the follower constraints over a‌ continuous support polyhedron. A‌ significant difficulty with this‌‌ problem is that the follower may have infinite‌ possible realizations of his‌ decision given the realization‌‌ of uncertainty, and coupling constraints can prevent any‌ follower optimal solutions from‌ being feasible for some‌‌ realizations. We model this explicitly through an extended‌ real recourse definition that‌ assigns an infinite value‌‌ when no follower optimal response can be paired‌ with the leader decision.‌ This separates robust feasibility‌‌ enforcement over the uncertainty set from the estimation‌ of expected performance. We‌ prove almost sure consistency‌‌ of the sample average approximation (SAA) of the‌ expected objective, and we‌ outline an algorithmic framework‌‌ that iteratively adds cuts to ensure feasibility for‌ all scenarios, and not‌ only for the sample‌‌ set.

8.3.4 Bidding in day-ahead electricity markets

Strategic‌ bidding has become a‌ central issue in deregulated‌‌ electricity markets, where producers and retailers trade electricity‌ in a day-ahead market‌ managed by a Market‌‌ Operator. Market participants submit bids specifying prices and‌ quantities, and the market‌ price is determined endogenously‌‌ from all submitted bids, creating significant profit uncertainty‌ due to unknown competitor‌ behavior. The paper 10‌‌ introduces a novel dynamic programming framework for a‌ Generation Company Stochastic Bidding‌ Problem under uncertainty in‌‌ competitors bids. The problem is shown to be‌ NP-hard, and two variants‌ are analyzed. First, a‌‌ relaxation yielding an upper bound is solved in‌ polynomial time. Second, a‌ fixed-quantity bidding problem is‌‌ studied, proven NP-hard, and solved to optimality in‌ pseudo-polynomial time. Computational results‌ on large, realistic instances‌‌ show that the fixed-quantity variant achieves solutions within‌ 1% of the upper‌ bound, demonstrating both efficiency‌‌ and solution quality.

9 Bilateral contracts and grants‌ with industry

9.1 Bilateral‌ contracts with industry

Distribution‌‌ network design - La‌ poste

Participants: Gaël Guillot, Diego Cattaruzza,‌ Frédéric Semet.

This project aims to address‌ network design for long-haul transportation. A PhD thesis‌ related to this project started in 2025.

9.2‌ Exploratory Research Actions and Technological Development Actions

9.2.1‌ Défi Inria-EDF : "Gérer les systèmes électriques de‌ demain"

Scientific leadership of the challenge (Inria): Luce‌ Brotcorne

The context of the challenge is defined‌ by several scenarios describing the evolution of the‌ French power system, developed by Réseau de Transport‌ d’Électricité (RTE) as part of the National Low-Carbon‌ Strategy (SNBC). Among the five proposed scenarios, a‌ massive integration of photovoltaic power can be observed—its‌ share increasing by a factor of 7 to‌ 22—as well as wind power, increasing by a‌ factor of 2.5 to 4. In addition, demand-side‌ flexibility is expected to play a key role,‌ representing between 15 and 19 GW (including Vehicle-to-Grid‌ (V2G), i.e. smart charging of electric vehicles).

In‌ this context, the challenge entitled “Managing the power‌ systems of tomorrow” aims to envision and develop‌ new tools (methods, regulatory approaches, algorithms, software) and‌ new sources of information (meters, sensors with higher‌ temporal and spatial resolution, high-resolution weather forecasts, electric‌ vehicle charging stations, mobile applications, etc.) to support‌ strategic and operational decision-making, enabling the economic, ecological,‌ and resilient management of future power systems in‌ the context of the ecological transition and climate‌ change.

Optimization methods for pricing in the electricity‌ sector within a regulated competitive environment

Participant: Luce‌ Brotcorne.

Co-supervision of a CIFRE PhD thesis‌ with the Tropical team.

Decomposition algorithms for long-term‌ investment problems

Participants: Marius Roland, Frédéric Semet‌.

Co-supervision of a postdoctoral researcher in collaboration‌ with the EDGE team at Inria Bordeaux.

Incentive‌ Design under Bounded Rationality

Participants: Hélène Le Cadre‌, Ashok Krishnan K. S.

Co-supervision of‌ a postdoctoral researcher in collaboration with Ana Busic‌ (Inria Paris, ARGO team and DI ENS).

Noncooperative‌ games with uncertain payoffs are traditionally studied under‌ expected-utility theory, which assumes fully rational agents. However,‌ human decision-makers often behave irrationally due to subjective‌ risk perception, motivating the use of prospect theory.‌ In this project, we aim to study the‌ impact of prospect-theoretic transformations on Nash equilibria, which‌ is still not fully understood. In aggregative games,‌ incorporating irrationality can preserve symmetric equilibria while eliminating‌ asymmetric ones, and some value functions map uncountable‌ equilibria to finite sets. These results can inform‌ equilibrium selection in human-in-the-loop systems, where comparing multiple‌ equilibria is necessary.

9.2.2 INRIA Défi with OVH‌ Cloud (2021-2025)

Participants: Luce Brotcorne, Nathalia Wolf‌.

Until now, cloud computing operators, such as‌ OVHcloud, have applied pricing strategies driven by the‌ reservation of virtualized resources. More precisely, OVHcloud offers‌ two types of services to its customers: VPS‌ (virtual private server) and Public Cloud. VPS is‌ a cost-effective solution for pre-production and production environments‌ that do not require constant performance. The PublicCloud‌ of OVHcloud offers a multi-server infrastructure with high machine availability. Unfortunately, the‌ resources reserved in the‌ Public Cloud are underutilized,‌‌ which can lead to energy inefficiency in the‌ infrastructure, while the VPS‌ favors an over-allocation of‌‌ hardware resources, not allowing resources, which does not‌ provide any guarantee of‌ performance for customers. The‌‌ research activity in this project aims at identifying‌ a viable balance between‌ these 2 options to‌‌ allow customers to benefit from guaranteed performance while‌ minimizing the energy footprint‌ of the OVHcloud infrastructure.‌‌ In particular, we want to determine discounts to‌ offer to customers to‌ encourage them to free‌‌ up resources when they do not need them,‌ to offer these available‌ resources to other customers-or‌‌ services-while smoothing the demand.

Through this new offer,‌ and its dynamic pricing,‌ we wish to maintain‌‌ a high performance criterion while eliminating the waste‌ of underutilized resources.

This‌ problem is a hierarchical‌‌ decision making process between a leader (OVH) and‌ followers (the two type‌ of customers). Bilevel optimizations‌‌ models are defined and solved to answer these‌ questions. The collaboration with‌ the Inria Spirals team‌‌ aims to measure cloud services energy consumptions.

In‌ the medium term, the‌ integration of renewable energy‌‌ production in the demand smoothing process could be‌ another research issue for‌ this work. This will‌‌ lead to the resolution of stochastic bilevel optimization‌ problems: www.inria.fr/fr/inria-ovhcloud

9.3 Bilateral‌ grants with industry

SAMOA,‌‌ Stackelberg Games for Flexibility (Dis)Aggregation

Participants: Hélène Le‌ Cadre [PI], Luce‌ Brotcorne, Yezekael Hayel‌‌ [ University of Avignon], Olivier Beaude [EDF]‌.

This project has‌ been extended up to‌‌ July 2025. SAMOA addressed peering issues by distributing‌ electricity generation across the‌ network instead of concentrating‌‌ it in a few locations. At the network‌ level, intermediate aggregation was‌ used to balance supply‌‌ and demand within subparts of the grid, such‌ as between nodes or‌ micro-grids. In the first‌‌ phase of SAMOA, a master’s intern from the‌ University of Montpellier developed‌ a small Python program‌‌ using a proximal method to compute Nash equilibria.‌ In the second phase,‌ SAMOA led to the‌‌ organization of the NETGCOOP conference at Inria Lille.‌

JEDI, game theory for‌ the electricity system decarbonization‌‌

Participants: Hélène Le Cadre [PI], Luce Brotcorne‌, Gael Guillot,‌ Alejandro Jofré [CMM, Chile]‌‌.

JEDI aims to integrate learning-based algorithms within‌ game-theoretic models of the‌ electricity market. On the‌‌ methodological side, the integration of pollution constraints and‌ learning-based algorithms into multi-leader‌ single follower Stackelberg games‌‌ represents a significant challenge. On the application side,‌ the approaches developed within‌ the project JEDI aim‌‌ at accelerating the decarbonisation of the electricity systems.‌

Integrated transit system with‌ Electric RIDE-sharing and Mobility‌‌ Pickup Stations in smart grid (ERIDE-MoPS)

Participants: Yue‌ Su (PI), Luce‌ Brotcorne, Wim van‌‌ Ackooij [EDF].

This is a one-year PGMO‌ project started from September‌ 2025 and ends at‌‌ September 2026

Grant from AID

Participants: Hélène Le‌ Cadre, Mathis Guckert‌.

Grant from the‌‌ innovation defense agency (AID)‌ of the DGA for the PhD topic "Allocation‌ of search efforts of a fleet of submarine‌ drones" leading to the hiring of Mathis Guckert‌ as PhD student.

This project focuses on modeling‌ and studying complex multi-agent systems, including swarms/fleets of‌ unmanned aerial/submersible vehicles, e.g., drones, performing missions, like‌ in rescuing problems, detecting or tracking lost objects/persons‌ whose locations are uknown to the searchers. Adversarial‌ situations may further be considered. Situated at the‌ intersection of game theory and multi-agent learning, the‌ project will explore distributed learning methods from artificial‌ intelligence, including multi-agent performative learning and reinforcement learning,‌ to support strategic mission planning. The aim is‌ to develop new classes of algorithms for finding‌ equilibria in time-varying information games.

PGMO IROE C‌ project TROMAT (2025-2026)

Participant: Marius Roland.

“Tractable‌ Stochastic Optimization through MAtrix Theory” in collaboration with‌ the AIP Continuous Optimization Team at RIKEN (Japan)‌ and EDF R&D (France). The TROMAT project addresses‌ the computational intractability of large-scale, two-stage stochastic optimization‌ problems, which are central to decision-making in energy‌ networks such as unit commitment and transmission expansion‌ planning. These problems face the "curse of dimensionality,"‌ where the number of variables and constraints grows‌ linearly with the number of scenarios used to‌ represent uncertainty. Building on a novel matrix-theoretic perspective,‌ TROMAT aims to unify diverse scenario clustering techniques,‌ specifically centroid-based and representative-based methods, by describing them‌ as either outer approximations (aggregating constraints) or inner‌ approximations (aggregating variables) defined by corresponding clustering matrices.‌ The project seeks to develop a unified mathematical‌ framework to examine how different clustering methods affect‌ model structure and solution quality, while leveraging numerical‌ linear algebra techniques like singular value decomposition and‌ randomized linear algebra to construct computationally efficient clusterings.‌ Furthermore, the research extends these matrix-based insights to‌ linear and mixed-integer programming contexts to enhance the‌ performance of heuristics and relaxations. By providing smaller,‌ reduced models that preserve essential information, TROMAT enables‌ faster solution times and more informed decision-making for‌ complex energy infrastructure.

10 Partnerships and cooperations

10.1‌ International initiatives

10.1.1 Inria associate team not involved‌ in an IIL or an international program

GALANGAL‌

Title:
GAme theoretic LeAriNG and optimizAtion for networked‌ eLectricity markets
Duration:
2023 -> 2025
Coordinators:
Hélène‌ Le Cadre and Michel Gendreau (michel.gendreay@polymti.ca)
Partners:
- Polytechnique‌ Montréal Montréal (Canada)
Inria contact:
Hélène Le Cadre‌
Summary:
GALANGAL is an Associate Team between the‌ INOCS Team of the Inria Lille-Nord Europe research‌ center and Polytechnique Montréal, in Canada. The focus‌ of GALANGAL is on the elaboration of methods‌ and algorithms for equilibrium computation, while taking into‌ account global, e.g., efficiency, robustness, group-based fairness criteria,‌ and local constraints, e.g., privacy, individual fairness criteria.‌ Within this project, the analysis of equilibria relies‌ on Game Theory. Game Theory is the study‌ of interacting decision markets. A large part of‌ the work in this area has focused on‌ equilibrium computation, but another relevant question is how‌ agents might reach an equilibrium, especially given that no single agent has‌ full information on the‌ state of the system‌‌ or full authority over the strategies of the‌ other agents. We will‌ consider two paradigms to‌‌ compute equilibria: i) decomposition methods with minimum information‌ exchange, ii) game-theoretic learning‌ based on performative prediction‌‌ . Though works in i) do not distinguish‌ between local and global‌ objectives, ii) enables to‌‌ capture the sift caused by the players' local‌ objectives on their global‌ strategies and on the‌‌ overall system performance. These two paradigms will be‌ compared, and new methods‌ and algorithms will be‌‌ developed. Two applications tracks will be considered: network‌ maintenance in the presence‌ of renewable energy sources‌‌ generating uncertainties, and equilibrium tracking within restructured electricity‌ markets.

BILENS

Title:
BIlevel‌ optimization for Logistics, ENergy‌‌ and Security problems
Duration:
January 1, 2025 –‌ December 31, 2027
Coordinators:‌
Luce Brotcorne , Alejandro‌‌ Jofre (Centro de Modelamiento Matemático)
Partners:
- Centro de‌ Modelamiento Matemático, Universidad de‌ Chile (Chile)
- Instituto Sistemas‌‌ Complejos de Ingeniería - ISCI (Chile)
- Universidad Técnica‌ Federico Santa María (Chile)‌
Inria contact:
Luce Brotcorne‌‌
Summary:

BILENS is an associated team between INOCS-‌ INRIA Lille and CMM/ISCI‌ Santiago de Chile.

The‌‌ team focuses on the development of new models‌ and solution methods for‌ problems that can be‌‌ addressed by bilevel programming / Stackelberg games. Bilevel‌ problems are constrained optimization‌ problems in which some‌‌ constraints specify that a subset of variables is‌ an optimal solution to‌ another (nested) optimization problem.‌‌ Stackelberg games are games in which an agent,‌ the leader, must commit‌ to a strategy that‌‌ the other agents, the followers, can observe before‌ committing to a strategy‌ of their own.

In‌‌ BILENS, problems of logistics, energy and security are‌ addressed. The study of‌ these problems, defined thanks‌‌ to the researchers' contacts with companies in these‌ fields, will allow the‌ development of relevant approaches‌‌ with potential transferability to industry.

10.1.2 STIC/MATH/CLIMAT AmSud‌ projects

SOGGA

Participants: Luce‌ Brotcorne, Hélène Le‌‌ Cadre, Francesco Morri.

Title:
Stochastic optimization,‌ generalized games and applications‌
Program:
MATH-AmSud
Duration:
January‌‌ 1, 2024 – December 31, 2025
Local supervisor:‌
Luce Brotcorne
Partners:
- Universidad‌ de O’Higgins
- Salas (Chili)‌‌
- Universidad del Pacífico
Inria contact:
Luce Brotcorne
Summary:‌
Chile, Peru and France,‌ as well as many‌‌ countries in South America and Europe, share a‌ very similar systems to‌ deal with their electricity‌‌ markets. In parallel, all three countries (together with‌ the rest of the‌ world) are being affected‌‌ by climate change in many aspects, such as‌ scarcity of water, intense‌ droughts, pollution and the‌‌ greenhouse effect, the necessity of new energy sources,‌ just to name a‌ few. To face these‌‌ challenges, we need new technology coming from many‌ fields of science. One‌ of such fields is‌‌ mathematics and in particular, stochastic optimization and game‌ theory. These theoretical fields‌ allow us to model‌‌ economic interactions, management solutions, optimal design and operations,‌ among many other relevant‌ aspects of Natural Resources‌‌ and Energy Management. In‌ the present project, we propose to develop new‌ theoretical and numerical advances in four research lines,‌ concerning Stochastic Optimization and Game Theory. Namely, we‌ will work on: 1) Continuity-like properties in Equilibrium‌ problems; 2) Regularity in Generalized Equilibrium problems; 3)‌ Bilevel games with decision-dependent uncertainty; and 4) Algorithms‌ and mechanism design in learning games. The four‌ research lines are strongly motivated by the aforementioned‌ applications.

10.1.3 Participation in other International Programs

IVADO-Inria‌ Initiative

Participant: Marius Roland.

Title:
Making Large-Scale‌ Stochastic Programs Tractable: From Scenario Clustering to Matrix‌ Theory
Partner Institution(s):
- IVADO
- Polytechnique Montréal
Date/Duration:
August‌ 7, 2025 – September 11, 2025
Summary:
This‌ project funded a five-week scientific collaboration between Marius‌ Roland (Inria Lille) and Prof. Thibaut Vidal (Polytechnique‌ Montréal). The research focused on addressing the curse‌ of dimensionality in decision-making under uncertainty by bridging‌ stochastic optimization and machine learning. Specifically, the project‌ aimed to develop a unified matrix-theoretic framework for‌ scenario clustering, utilizing matrix decomposition techniques and randomized‌ linear algebra to create tractable approximations for complex‌ energy and supply chain models. In addition to‌ these scientific objectives, the visit served as a‌ strategic foundation to structure a future Inria Associate‌ Team application and foster student mobility between the‌ two institutions.

ECOS-Sud

Participants: Luce Brotcorne, Martine‌ Labbé, Marius Roland, Frédéric Semet.‌

Title:
Data-driven decision-making in location and transportation
Partner‌ Institution(s):
- Universidad de O’Higgins
- Universidad de Santiago de‌ Chile
Date/Duration:
2024 – 2026
Summary:
This project‌ aims to study mathematical models for data-driven decision-making‌ in location and transportation settings and develop ad-hoc‌ algorithms to compute decisions in realistic instances efficiently.‌ We also aim to compare different approaches to‌ facing uncertainty using data, comparing stochastic programming methods,‌ robust optimization, and decision-focused learning methods in order‌ to prescribe to decision-makers what is the best‌ model to use in each situation. We make‌ the distinction between NP-hard problems and polynomial time‌ solvable problems, taking into account the decisions prescribed‌ and the computational times to get an answer.‌

10.2 International research visitors

10.2.1 Visits of international‌ scientists

Other international visits to the team

Walter‌ Rei

Status:
Professor
Institution of origin:
Quebec University‌ at Montréal
Country:
Canada
Dates:
September
Context of‌ the visit:
work on Stochastic optimization and decomposition‌ methods
Mobility program/type of mobility:
Research stay -‌ Masters internship co-supervision

Alejandro Jofre

Status:
Professor
Institution‌ of origin:
CMM - Universidad de Chile
Country:‌
Chili
Dates:
June
Context of the visit:
Learning‌ games
Mobility program/type of mobility:
Research stay

Carlos‌ Antil Catripay

Status:
Master student
Institution of origin:‌
Universidad de Chile
Country:
Chili
Dates:
January -‌ April
Context of the visit:
Learning games
Mobility‌ program/type of mobility:
Research stay

Ricardo Ignacio Barriga‌ Vera

Status:
Master student
Institution of origin:
Universidad‌ de Chile
Country:
Chili
Dates:
January - April‌
Context of the visit:
Learning games
Mobility program/type‌ of mobility:
Research stay

Miguel Anjos

Status:
Professor‌
Institution of origin:
University of Edinburgh
Country:
Scotland
Dates:
21-25 July
Context‌ of the visit:
project‌ with EDF on maintenance‌‌ scheduling
Mobility program/type of mobility:
Research stay

Ezra‌ Daniels

Status:
Master student‌
Institution of origin:
University‌‌ of Edinburgh
Country:
Scotland
Dates:
21-25 July
Context‌ of the visit:
project‌ with EDF on maintenance‌‌ scheduling
Mobility program/type of mobility:
Research stay

Pablo‌ Torrealba-González

Status:
researcher
Institution‌ of origin:
School of‌‌ Industrial Engineering, Pontificia Universidad Católica de Valparaíso
Country:‌
Chile
Dates:
from 21/07/2025‌ to 25/07/2025 and from‌‌ 10/09/2025 to 17/09/2025
Context of the visit:
work‌ on human factors in‌ order batching and picker‌‌ routing problems
Mobility program/type of mobility:
research stay‌

10.2.2 Visits to international‌ teams

Research stays abroad‌‌

Luce Brotcorne

Visited institution:
Universidad de Chile, Universidad‌ Tecnica Federico Santa Maria‌
Country:
Chile
Dates:
from‌‌ 01/12/2025 to 07/12/2025
Context of the visit:
work‌ in the context of‌ BILENS
Mobility program/type of‌‌ mobility:
research stay

Gaël Guillot

Visited institution:
UQAM,‌ Montréal
Country:
Canada
Dates:‌
from 06/12/2025 to 14/12/2025‌‌
Context of the visit:
work on decomposition methods‌
Mobility program/type of mobility:‌
research stay

Marius Roland‌‌

Visited institution:
Ivado, Cirrelt, Polytechnique Montréal
Country:
Canada‌
Dates:
from 07/08/2025 to‌ 11/09/2025
Context of the‌‌ visit:
work on drafting associate team and branch-and-bound‌ tree closure
Mobility program/type‌ of mobility:
research stay‌‌

Marius Roland

Visited institution:
The University of Tokyo,‌ RIKEN AIP
Country:
Japan‌
Dates:
from 01/11/2025 to‌‌ 06/11/2025
Context of the visit:
work on random‌ projections for low-rank linear‌ programs
Mobility program/type of‌‌ mobility:
research stay

Marius Roland

Visited institution:
Université‌ Adolfo-Ibáñez, Universidad de Chile‌
Country:
Chile
Dates:
from‌‌ 07/12/2025 to 22/12/2025
Context of the visit:
MIP‌ conference & work on‌ bilevel interdiction games
Mobility‌‌ program/type of mobility:
conference and research stay

Maxime‌ Ogier

Visited institution:
School‌ of Industrial Engineering, Pontificia‌‌ Universidad Católica de Valparaíso
Country:
Chile
Dates:
from‌ 15/12/2025 to 19/12/2025
Context‌ of the visit:
work‌‌ on human factors in order batching and picker‌ routing problems
Mobility program/type‌ of mobility:
research stay‌‌

Frédéric Semet

Visited institution:
Universidad O'Higgins, Universidad de‌ Chile
Country:
Chile
Dates:‌
from 13/12/2025 to 20/12/2025‌‌
Context of the visit:
WOA workshop & research‌ work on bilevel programming‌
Mobility program/type of mobility:‌‌
research stay

10.3 European initiatives

10.3.1 Horizon Europe‌

Participants: Luce Brotcorne,‌ Hélène Le Cadre,‌‌ Gaël Guillot, Rebeca Murillo, Barbara Rodrigues‌, Mesut Can Koseoglu‌, Zhenyu Wu.‌‌

SUM project on cordis.europa.eu

Title:
SEAMLESS SHARED URBAN‌ MOBILITY
Duration:
From June‌ 1, 2023 to May‌‌ 31, 2026
Partners:
- INSTITUT NATIONAL DE RECHERCHE EN‌ INFORMATIQUE ET AUTOMATIQUE (INRIA),‌ France
- TRANSPORTS PUBLICS GENEVOIS,‌‌ Switzerland
- SIXT GMBH & CO. AUTOVERMIETUNG KG, Germany‌
- HYDROLIFT SMART CITY FERRIES‌ AS (HYDROLIFT SMART CITY‌‌ FERRIES AS), Norway
- FREDRIKSTAD KOMMUNE, Norway
- SIEMENS MOBILITY‌ BV, Netherlands
- ROTTERDAMSE ELEKTRISCHE‌ TRAM NV (RET NV),‌‌ Netherlands
- ETHNICON METSOVION POLYTECHNION (NATIONAL TECHNICAL UNIVERSITY OF‌ ATHENS - NTUA), Greece‌
- INSTITUT VEDECOM (VEDECOM), France‌‌
- TECHNISCHE UNIVERSITAET MUENCHEN (TUM), Germany
- DIMOS PENTELIS (MUNICIPALITY‌ OF PENTELI), Greece
- GMINA‌ MIEJSKA KRAKOW - MIASTO‌‌ NA PRAWACH POWIATU (MUNICIPALITY‌ OF KRAKOW UMK), Poland
- UNIVERSITEIT TWENTE (UNIVERSITEIT TWENTE),‌ Netherlands
- POLIS (POLIS), Belgium
- MUNICIPALITY OF JERUSALEM, Israel‌
- FREE NOW HELLAS MONOPROSOPI AE (FREENOW HELLAS SINGLE‌ MEMBER SA), Greece
- SERVICOS MUNICIPALIZADOS DE TRANSPORTES URBANOS‌ DE COIMBRA (SMTUC), Portugal
- MUNICIPIO DE COIMBRA (CAMARA‌ MUNICIPAL DE COIMBRA), Portugal
- TEL AVIV UNIVERSITY (TAU),‌ Israel
- NEXTBIKE CY LTD, Cyprus
- LPT LARNACA PUBLIC‌ TRANSPORTSERVICES AND OPERATIONS LIMITED, Cyprus
- CONCESIONES UNIFICADAS SL‌ (CONCESIONES UNIFICADAS SLU), Spain
- EUROPEAN ROAD TRANSPORT TELEMATICS‌ IMPLEMENTATION COORDINATION ORGANISATION - INTELLIGENT TRANSPORT SYSTEMS &‌ SERVICES EUROPE (ERTICO ITS EUROPE), Belgium
- MOBY X‌ SOFTWARE LIMITED (MOBY), Cyprus
- LANDESHAUPTSTADT MUNCHEN, Germany
- TECHNISCHE‌ UNIVERSITEIT DELFT (TU Delft), Netherlands
- SIGMA 6 LTD,‌ Israel
- ZF CV Systems Global GmbH (ZF CV‌ Systems Global GmbH), Switzerland
- UNIWERSYTET JAGIELLONSKI, Poland
- CHALMERS‌ TEKNISKA HOGSKOLA AB, Sweden
Inria contact:
Luce Brotcorne‌
Coordinator:
Summary:
The objective of SUM is to‌ transform current mobility networks towards innovative and novel‌ shared mobility systems (NSM) integrated with public transport‌ (PT) in more than 15 European Cities by‌ 2026 reaching 30 by 2030. Intermodality, interconnectivity, sustainability,‌ safety, and resilience are at the core of‌ this innovation. The outcomes of the project offer‌ affordable and reliable solutions considering the needs of‌ all stakeholders such as end users, private companies,‌ public urban authorities. SUM project will develop five‌ pillars consisting of technological, co-creation, and policy tools‌ to tackle the identified NSM barriers for a‌ typical, car-focused family. These five pillars can increase‌ the modal share of NSM via targeted push/pull‌ measures and policy recommendations. SUM will introduce a‌ federation of solutions including prediction, scheduling, integrated NSM-PT‌ ticketing, and real-time NSM management. This created ecosystem‌ will reduce the total door-to-door travel times using‌ integrated NSM-PT. This can change the behavior of‌ 34% of travelers using cars and 17% of‌ travelers sceptic about using NSM. The partners in‌ this diverse consortium have access to innovative tools‌ and expertise making them uniquely positioned to tackle‌ the barriers in 9 living labs and 30‌ organizations across Europe.

10.4 National initiatives

10.4.1 ANR‌

ANR project ADELE (2022-2025): “Resource Allocation in City‌ Logistics with Demand Uncertainty” in collaboration with LCOMS‌ (Univ. of Lorraine), Toulouse Business School, Colisweb. A‌ central issue in City Logistics (CL) is to‌ design logistics systems that move goods to, from,‌ and within urban areas while meeting sustainability goals.‌ A central role is played by the orchestrator.‌ The orchestrator is the stakeholder that operates and‌ organizes a CL system when multiple stakeholders are‌ implied. In ADELE, we tackle the planning problem‌ faced by the orchestrator in coordinating and managing‌ the resources offered by carriers or logistics service‌ providers. The problem aims to determine what logistics‌ facilities should be used and when and where‌ the vehicles of the carriers should be assigned‌ to cover the demand in the most efficient‌ way. A key feature is that demand is‌ uncertain. We consider two main variants depending on‌ whether the CL system is one or two tiers. ADELE aims to‌ develop new efficient mathematical‌ models and decision support‌‌ methods. We aim to design and implement ad-hoc‌ optimization algorithms based on‌ mathematical modeling. This project‌‌ is a continuation of the INRIA Innovation Lab‌ Colinocs.

10.5 Regional initiatives‌

10.5.1 Project STaRS SITAR‌‌

The SITAR project led by Hélène Le Cadre‌ and funded by region‌ Hauts-de-France dealt with behavioral‌‌ game theory and bounded rationality. The project led‌ to the hiring of‌ a postdoc researcher Frédy‌‌ Pokou . The project ended in July 2025.‌

10.5.2 Project Blooe (Multi-criteria‌ optimization of energy exchanges‌‌ in a local renewable energy community via blockchain‌ ), Energie Electrique 4.0,‌ funded by Région Haut‌‌ de France

The partners are Junia Grande Ecole‌ d’Ingénieurs de Lille &‌ INRIA Lille - Nord‌‌ Europe. It is les by Luce Brotcorne at‌ INRIA and led to‌ the hiring of 2‌‌ post doc and 1 phd student.

In recent‌ years, electricity grids have‌ undergone significant changes, notably‌‌ the integration of renewable energy sources into distribution‌ networks. These new types‌ of generators are integrated‌‌ as close as possible to consumers and create‌ new energy flows on‌ the electricity grids. In‌‌ addition, new players are emerging: prosumers (consumers who‌ also produce their own‌ electricity), electric vehicles, and‌‌ storage systems. We are therefore seeing the emergence‌ of local energy communities,‌ which bring together these‌‌ different types of players within a distribution network.‌ These players share decentralized‌ production (particularly photovoltaic) within‌‌ collective self-consumption operations. The larger-scale development of these‌ energy communities requires methods‌ to optimize energy exchanges‌‌ between players.

11 Dissemination

11.1 Promoting scientific activities‌

11.1.1 Scientific events: organisation‌

Member of the conference‌‌ program committees

TPC member the AAAI 25: Hélène‌ Le Cadre
Steering committee‌ member for NETGCOOP 2025:‌‌ Hélène Le Cadre
Stream "Pricing and Revenue Management‌ Innovations" in EURO conference‌ 2025: Yue Su &‌‌ Luce Brotcorne
Triennal Symposium of Transportation Analysis -‌ TRISTAN 2025: Frédéric Semet‌
IEEE/IFAC CoDIT 2025: Frédéric‌‌ Semet
IC-TORS 2025 : Frédéric Semet
ROADEF 2025‌ : Luce Brotcorne ,‌ Maxime Ogier , Frédéric‌‌ Semet

11.1.2 Journal

Member of the editorial boards‌

Computers & Operations Research‌ Optimization: Luce Brotcorne –‌‌ Member of the editorial advisory board.
International Transactions‌ in Operations Research: Luce‌ Brotcorne – Associate editor.‌‌
Journal of Optimization Theory and Applications (JOTA): Martine‌ Labbé - Area editor.‌
Open Journal of Mathematical‌‌ Optimization: Martine Labbé - Member of the steering‌ committee.
Transportation Science: Martine‌ Labbé - Member of‌‌ the advisory board.
International Transactions in Operations Research:‌ Martine Labbé - Associate‌ editor.
European Journal of‌‌ Computational Optimization: Martine Labbé - Associate editor.

Reviewer‌ - reviewing activities

European‌ Journal of Operational Research,‌‌ Computers & Operations Research, Networks, Transportmetrica B: Transport‌ Dynamics, Transportation Research Part‌ E, International Transaction in‌‌ Operational Research, Dynamic Games and Applications; International Game‌ Theory Review, JOTA, IEEE‌ Transactions on Automatic Control,‌‌ IEEE Control Systems Letters; Engineering Applications of Artificial‌ Intelligence, IEEE Transactions on‌ Control of Network Systems,‌‌ IEEE Transactions on Network‌ Science and Engineering, Optimization Letters : Gaël Guillot‌ , Hélène Le Cadre , Maxime Ogier ,‌ Frédéric Semet , Yue Su , Luce Brotcorne‌ , Martine Labbé

11.1.3 Leadership within the scientific‌ community

ROADEF (French OR association): Maxime Ogier ,‌ member of the board.
PGMO: Luce Brotcorne ,‌ member of the scientific committee.
INFORMS, Energy Natural‌ Resources and the Environmentatl Section: Luce Brotcorne ,‌ secretary & treasurer.
EURO Working Group “Pricing and‌ revenue management”: Luce Brotcorne , coordinator.
EURO Working‌ Group “Vehicle Routing and Logistics Optimization (VEROLOG)”: Frédéric‌ Semet , member of the board.
GdR Recherche‌ Opérationnelle et Décision: Frédéric Semet , member of‌ the board.

11.1.4 Scientific expertise

Reviewer for ANR‌ project: Hélène Le Cadre
Reviewer for ANR project:‌ Luce Brotcorne
Reviewer for research proposals for 2025‌ in the field of groundbreaking research in artificial‌ intelligence for the ministry of innovation, science and‌ technology of Israel: Hélène Le Cadre
IVADO International‌ Advisory Committee, Canada: Martine Labbé - Member
Scientific‌ orientation committee of the Interuniversity Centre on Entreprise‌ Networks, Transportation and Logistics (CIRRELT), Canada: Frédéric Semet‌ - Member
EURO Gold Medal selection committee- Association‌ of the European Operational Research Societies: Martine Labbé‌ - Member

11.1.5 Research administration

Vice Chair of‌ the Inria Evaluation Committee: Luce Brotcorne .
Elected‌ Member of the Inria Evaluation Committee: Hélène Le‌ Cadre .
Member of the “Commission Emploi Recherche”(CER)‌ at Inria Lille: Hélène Le Cadre .
Mission‌ Jeunes Chercheurs responsible for Inria Lille Center: Hélène‌ Le Cadre .
Deputy-director of CRIStAL: Frédéric Semet‌ .
Member of the “Commission des Utilisateurs des‌ Moyens Informatiques”(CUMI) at Centre Inria de l'Univeristé de‌ Lille: Gaël Guillot

11.2 Teaching - Supervision -‌ Juries - Educational and pedagogical outreach

11.2.1 Teaching‌

Licence: Gaël Guillot , Maxime Ogier , Object-Oriented‌ Programming, 96hrs, L2, Centrale Lille.
Licence: Gaël Guillot‌ , Maxime Ogier , Object-Oriented Programming, 48hrs, L3,‌ Centrale Lille.
Licence: Frédéric Semet , Advanced programming‌ and Complexity, 24hrs, L3, Centrale Lille.
Master: Maxime‌ Ogier , Frédéric Semet , Object-Oriented Programming, 32hrs,‌ M1, Centrale Lille.
Master: Maxime Ogier , Operations‌ Research, 24hrs, M1, Centrale Lille.
Master: Maxime Ogier‌ , Research in Computer Sciences, 24hrs, M2, Centrale‌ Lille.
Master: Frédéric Semet , Prescriptive analytics and‌ optimization, 64hrs, M1, Centrale Lille.
Master: Frédéric Semet‌ , Supply Chain Management and Operations Research ,‌ 44 hrs, M2, Centrale Lille.
Master: Frédéric Semet‌ , Operations Research, 24 hrs, M2, Centrale Lille.‌
Licence: Yue Su , Initiation au développement, 65h,‌ L1, Univeristé de Lille;
Licence: Yue Su ,‌ Introduction aux bases de données et SQL, 72h,‌ L1, Univeristé de Lille;
Licence: Yue Su ,‌ Nouveaux paradigmes de BDD, 23h, L1, Univeristé de‌ Lille;
Licence: Yue Su , Exploitation d’une base‌ de données, 32h, L1, Univeristé de Lille;
Licence:‌ Yue Su , Qualité et au delà du‌ relationnel, 14h, L1, Univeristé de Lille;

11.2.2 Supervision‌

PhD completed: Pablo Torrealba-González , Order batching and‌ picker routing in warehouses taking into account human factors 45, March‌ 2025, Dominique Feillet (Mines‌ de Saint Etienne), Maxime‌‌ Ogier , Frédéric Semet .
PhD completed: Juan‌ Sepulveda , New optimization‌ models and algorithms to‌‌ represent energy exchanges in local energy communities, June‌ 2025, Hélène Le Cadre‌ , Luce Brotcorne .‌‌
PhD completed: Wenjiao Sun , Models and algorithms‌ for integrated vehicle routing‌ and driver scheduling problems‌‌ 44, October 2025, Maxime Ogier , Frédéric‌ Semet .
PhD completed:‌ Francesco Morri , Game-theoretic‌‌ learning in intelligent marketplaces 43, December 2025,‌ Hélène Le Cadre ,‌ Luce Brotcorne .
PhD‌‌ completed: Tifaout Almeftah , Models and algorithms for‌ group testing, December 2025,‌ Diego Cattaruzza (University of‌‌ Udine, Italy), Martine Labbé, Frédéric Semet .
PhD‌ completed: Aitor Lopez Sanchez‌ , Distributed, scalable, and‌‌ efficient fleet coordination for agriculture mobile robots 42‌, September 2025, Frédéric‌ Semet , Marin Lujak‌‌ (University Rey Juan Carlos, Spain).
PhD in progress:‌ Luis Rojo , Incentive‌ mechanisms for electric vehicle‌‌ charging, from October 2021, Luce Brotcorne , Michel‌ Gendreau (Polytechnique Montréal, Canada),‌ Miguel Anjos (University of‌‌ Edinburgh, UK).
PhD in progress: Natalia Wolf ,‌ Towards energy-based pricing strategies‌ for the cloud, from‌‌ April 2023, Luce Brotcorne .
PhD in progress:‌ Mesut Can Koseoglu ,‌ Designing incentives for better‌‌ integration of shared urban mobility within public transport,‌ from February 2024, Luce‌ Brotcorne , Shadi Sharif‌‌ Azadeh (TU Delft, Netherlands).
PhD in progress: Zhenyu‌ Wu , Network Design‌ of Bike Sharing Systems‌‌ with Public Transit Integration, from March 2024, Luce‌ Brotcorne , Shadi Sharif‌ Azadeh (TU Delft, Netherlands).‌‌
PhD in progress: Adrien Belfer , Résolution de‌ problème d’optimisation bi-niveaux sous‌ incertitude, from September 2024,‌‌ Luce Brotcorne , Marius Roland .
PhD in‌ progress: Salma Janati ,‌ Integration of forecasting methods‌‌ into optimization algorithms: application to urban logistics, from‌ October 2024, Luce Brotcorne‌ , Frédéric Semet .‌‌
PhD in progress: Mathis Guckert , Allocation of‌ search efforts for a‌ fleet of underwater drones,‌‌ from January 2025, Hélène Le Cadre , Luce‌ Brotcorne .
PhD in‌ progress: Nathan Davouse ,‌‌ Ecological and economic logistics service network design: Models‌ and Decision Support Algorithms,from‌ March 2025, Diego Cattaruzza‌‌ (University of Udine, Italy),Gaël Guillot ,Frédéric‌ Semet

11.2.3 Juries

Panagiotis‌ Andrianesis, HDR, Université de‌‌ Grenoble, Selected Topics in Power System Analysis, Optimization‌ and economics, Luce Brotcorne‌ - Reviewer
Luis Lopes‌‌ Marques, PhD, Université de Bordeaux, Méthodes d’agrégation pour‌ des formulations de programmation‌ dynamique de très grande‌‌ taille: Gaël Guillot - Examiner
Louise Sadoine, PhD,‌ Université de Mons, Belgique,‌ Noncooperative game theory for‌‌ resources scheduling and planning in renewable energy communities:‌ Hélène Le Cadre -‌ Reviewer
Erick Velasquez, Master‌‌ 2 internship at Pontifica Universidad Catolica de Chile:‌ Hélène Le Cadre -‌ Reviewer
Luca Santosuosso, PhD,‌‌ Université Paris sciences et lettres, Distributed Stochastic Optimization‌ for Operating Complex Virtual‌ Power Plants : Leveraging‌‌ Cascaded Run-of-the-River Hydropower Flexibility for Renewable Energy Integration:‌ Luce Brotcorne - Chairperson‌
Emanuele Concas, PhD, Marne-la-vallée‌‌ ENPC, Pricing Bundles for‌ Airline Revenue Management: Luce Brotcorne - Examiner
Eric‌ Larsen, PhD, Université de Montréal, Machine Learning Accelerated‌ Stochastic Optimization and Applications to Railways Operations: Frédéric‌ Semet - Reviewer
Yuji Zou, PhD, Université d'Angers,‌ Metaheuristic Algorithms for Routing Problems: Frédéric Semet -‌ Reviewer
Yerlan Kuzbakov, Université Cergy Paris Université, Three‌ essays on facility location and vehicle routing for‌ modern transportation networks including robotic last-mile delivery: Frédéric‌ Semet - Reviewer
Isaac Balster, Université de Bordeaux,‌ Méthodes de branch-cut-and-price pour le problème joint de‌ routage et de gestion des stocks: Frédéric Semet‌ - Examiner
Kehinde Ganiyu Ismaila, Khalifa University, Optimization‌ models and algorithms for e-commerce fulfillment problems with‌ order consolidation : Frédéric Semet - Reviewer

12‌ Scientific production

12.1 Major publications

1 articleN.‌Nabil Absi, D.Diego Cattaruzza, D.‌Dominique Feillet, M.Maxime Ogier and F.‌Frédéric Semet. A heuristic branch-cut-and-price algorithm for‌ the ROADEF/EURO challenge on Inventory Routing.Transportation‌ Science2019HAL
2 articleM.Mathieu Besançon‌, M.Miguel Anjos and L.Luce Brotcorne‌. Robust bilevel optimization for near-optimal lower-level solutions‌.Journal of Global Optimization904July‌ 2024, 813-842HALDOI
3 articleL.‌Luce Brotcorne, D.Didier Aussel, S.‌Sébastien Lepaul and L.Léonard Von Niederhäusen.‌ A Trilevel Model for Best Response in Energy‌ Demand-Side Management.European Journal of Operational Research‌2020HAL
4 articleL.Luce Brotcorne,‌ F.Fabien Cirinei, P.Patrice Marcotte and‌ G.Gilles Savard. An exact algorithm for‌ the network pricing problem.Discrete Optimization8‌22011, 246--258URL: https://dx.doi.org/10.1016/j.disopt.2010.09.003
5 article‌C.Carlos Casorrán, B.Bernard Fortz,‌ M.Martine Labbé and F.Fernando Ordóñez.‌ A study of general and security Stackelberg game‌ formulations.European Journal of Operational Research278‌32019, 855 - 868HAL DOI‌
6 articleD.Diego Cattaruzza, M.Martine‌ Labbé, M.Matteo Petris, M.Marius‌ Roland and M.Martin Schmidt. Exact and‌ Heuristic Solution Techniques for Mixed-Integer Quantile Minimization Problems‌.INFORMS Journal on Computing2024HAL
7‌ articleM.Maria Restrepo, F.Frédéric Semet‌ and T.Thomas Pocreau. Integrated Shift Scheduling‌ and Load Assignment Optimization for Attended Home Delivery‌.Transportation Science532019, 917-1212HAL‌

12.2 Publications of the year

International journals

8‌ articleH.Haider Ali, R.Reza Razi‌, B.Bruno Francois and L.Luce Brotcorne‌. Sustainable suburban mobility: Shared autonomous electric vehicles‌ day-ahead transit and charging optimization using TOU rates‌ and renewable energy.Sustainable Energy, Grids and‌ Networks42June 2025, 101703HAL DOI‌
9 articleM. F.Miguel F Anjos,‌ L.Luce Brotcorne and G.Gaël Guillot.‌ Optimal Electric Vehicle Charging with Dynamic Pricing, Customer‌ Preferences and Power Peak Reduction.INFOR: Information‌ Systems and Operational Research January 2025. In‌ press. HAL DOI back to text
10 articleJ.Jérôme de Boeck‌, B.Bernard Fortz‌, M.Martine Labbé‌‌, É.Étienne Marcotte, P.Patrice Marcotte‌ and G.Gilles Savard‌. Bidding in day-ahead‌‌ electricity markets: A dynamic programming framework.Computers‌ and Operations Research179‌July 2025HAL DOI‌‌back to text
11 articleP.Pamela Bustamante-Faúndez‌, V.Víctor Bucarey‌ L, M.Martine‌‌ Labbé, V.Vladimir Marianov and F.Fernando‌ Ordoñez. Novel valid‌ inequalities and branch-and-price for‌‌ Stackelberg security games.Computers and Operations Research‌183November 2025HAL‌DOI back to text‌‌
12 articleS.Sebastián Dávila-Gálvez, M.Martine‌ Labbé, V.Vladimir‌ Marianov, F.Fernando‌‌ Ordónẽz and F.Frédéric Semet. A Mixed-Integer‌ Optimization Formulation for Buyers‌ Formation.Computers and‌‌ Operations Research1831071812025HAL DOI back‌ to text
13 article‌P.Pablo Escalona,‌‌ L.Luce Brotcorne, B.Bernard Fortz and‌ N.Nathalia Wolf.‌ Spot-fare inspection in urban‌‌ bus transportation systems: strategy and unpredictability under a‌ Stackelberg game approach.‌Public Transport : Planning‌‌ and OperationsOctober 2025HAL DOI
14 article‌M.Marta Fochesato,‌ F.Frédy Pokou,‌‌ H.Hélène Le Cadre and J.John Lygeros‌. Noncooperative Games with‌ Prospect Theoretic Preferences.‌‌IEEE Control Systems Letters9702025,‌ 7125-7140HAL back to‌ text
15 articleM.‌‌Mathis Guckert, H.Hélène Le Cadre and‌ J.Jean Le Hénaff‌. A Generalized Potential‌‌ Game Approach of UAV Swarm Coordination for Hidden‌ Target Localization.IEEE‌ Control Systems Letters2025‌‌. In press. HALback to text
16‌ articleH.Hélène Le‌ Cadre, M.Mandar‌‌ Datar, M.Mathis Guckert and E.Eitan‌ Altman. Learning Market‌ Equilibria Preserving Statistical Privacy‌‌ Using Performative Prediction.IEEE Transactions on Automatic‌ Control7011November‌ 2025, 7125-7140HAL‌‌DOI back to text
17 articleA.Aitor‌ López-Sánchez, M.Marin‌ Lujak, F.Frédéric‌‌ Semet and H.Holger Billhardt. Fair and‌ efficient multi-agent routing for‌ cooperative and autonomous agricultural‌‌ fleets with implements.Computers and Operations Research‌185January 2026,‌ 107252HAL DOI back‌‌ to text
18 articleV.Vincent Moron,‌ E.Erwan Cornillault,‌ H.Haider Ali,‌‌ H.Hayley Fowler and A.Andrew Robertson.‌ A climatology of local‌ hourly wet spells across‌‌ the tropics.Climate Dynamics636June‌ 2025, 244HAL‌DOI
19 articleM.‌‌Matteo Petris, C.Claudia Archetti, D.‌Diego Cattaruzza, M.‌Maxime Ogier and F.‌‌Frédéric Semet. A Branch-Price-and-Cut algorithm for the‌ Kidney Exchange Problem.‌INFORMS Journal on Computing‌‌September 2025HAL back to text
20 article‌M.Matteo Petris,‌ C.Claudia Archetti,‌‌ D.Diego Cattaruzza, M.Maxime Ogier and‌ F.Frédéric Semet.‌ A tutorial on Branch-Price-and-Cut‌‌ algorithms.4OR: A Quarterly Journal of Operations‌ Research231March‌ 2025, 1-52HAL‌‌DOI back to text‌
21 articleT.Thibault Prunet, N.Nabil‌ Absi and D.Diego Cattaruzza. The Storage‌ Location Assignment and Picker Routing Problem: A Generic‌ Branch-Cut-and-Price Algorithm.European Journal of Operational Research‌3273December 2025, 857-874HAL DOI‌

International peer-reviewed conferences

22 inproceedingsL.Luce Brotcorne‌, M.Mesut Koseoglu and S.Shadi Sharif-Azadeh‌. Traveler-Centric Pricing and Trip Recommendation for Integrated‌ Shared Urban Mobility: A Bilevel Optimization Framework..‌EWG-RMP 2025 - Biennial Meeting of the EURO‌ Working Group on Revenue Management and PricingLuxembourg,‌ LuxembourgDecember 2025HALback to text
23‌ inproceedingsA.Ashok Krishnan K. S., H.‌Hélène Le Cadre and A.Ana Bušić.‌ How Irrationality Shapes Nash Equilibria: A Prospect-Theoretic Perspective‌.Proceedings of 64th IEEE Conference on Decision‌ and Control (CDC) 202564th IEEE Conference on‌ Decision and Control (CDC) 2025Rio de Jaineiro,‌ Brazil2025, 4428-4433HAL back to text‌
24 inproceedingsA.Ashok Krishnan, H.Hélène‌ Le Cadre and A.Ana Bušić. Achieving‌ a Collective Target Through Incentives.Network Games,‌ Artificial Intelligence, Control and Optimization: 12th International Conference,‌ NETGCOOP 2025, Bilbao, Spain, October 8–10, 2025, Proceedings‌NETGCOOP 2025 - 12th International Conference of Networks,‌ Games, Control and OptimizationBilbao, SpainNovember 2025‌HAL DOI back to text back to text‌
25 inproceedingsA.Aitor López-Sánchez, M.Marin‌ Lujak, F.Frederic Semet and H.Holger‌ Billhardt. Capacitated Agriculture Fleet Vehicle Routing with‌ Implements and Limited Autonomy: A model and a‌ two-phase solution approach.2025 IEEE International Conference‌ on Robotics and Automation (ICRA)Atlanta, United States‌IEEEMay 2025, 7160-7166HAL DOI back‌ to text
26 inproceedingsA. B.Abdellah Bulaich‌ Mehamdi, W.Wim van Ackooij, L.‌Luce Brotcorne, S.Stéphane Gaubert and Q.‌Quentin Jacquet. Duality between polyhedral approximation of‌ value functions and optimal quantization of measures.‌CDC 2025 - 64th IEEE Conference on Decision‌ and ControlRio, BrazilSeptember 2025HAL
27‌ inproceedingsF.Francesco Morri, H.Hélène Le‌ Cadre and L.Luce Brotcorne. Learning in‌ Stackelberg Conjectural Games.ROADEF 2025Champs-sur-Marne, France‌February 2025HAL back to text
28 inproceedings‌Y.Yue Su, A.Antoine Désir and‌ A.Axel Parmentier. Learning-based pricing policy for‌ substitute itineraries.MSOM 2025 - The INFORMS‌ Manufacturing and Service Operations Management annual conferenceLondon,‌ United KingdomJune 2025HAL back to text‌

Conferences without proceedings

29 inproceedingsL.Luce Brotcorne‌, M.Mesut Koseoglu and S.Shadi Sharif-Azadeh‌. A Bilevel Optimization Framework for Pricing and‌ Trip Allocation in Shared Mobility Networks.EURO‌ 2025---34th Conference of the Association of European Operational‌ Research SocietiesLeeds (Angleterre), United KingdomJune 2025‌HAL back to text
30 inproceedingsL.Luce‌ Brotcorne, Z.Zhenyu Wu and S.Shadi‌ Sharif-Azadeh. Designing Service Regions for Bike Sharing‌ Systems: Integration with Public Transport Networks.EURO 2025---34th Conference of the‌ Association of European Operational‌ Research SocietiesLeeds, Angleterre,‌‌ United KingdomJune 2025HAL back to text‌
31 inproceedingsS.Salma‌ Janati and F.Frédéric‌‌ Semet. Demand Modeling and Forecasting Methods for‌ Delivery between Pickup and‌ Drop-off Zones.VEROLOG‌‌ 2025Trento (Italie), ItalyJune 2025HAL
32‌ inproceedingsF.Francesco Morri‌, H.Hélène Le‌‌ Cadre and L.Luce Brotcorne. Learning in‌ Conjectural Stackelberg Games.‌GAIW Workshop 2025Detroit‌‌ (MI), United StatesMay 2025HAL back to‌ text
33 inproceedingsM.‌Matteo Petris, C.‌‌Claudia Archetti, D.Diego Cattaruzza, M.‌Maxime Ogier and F.‌Frédéric Semet. A‌‌ Branch-Price-and-Cut Algorithm for the Kidney Exchange Problem.‌EURO 2025 - 34th‌ Conference of the Association‌‌ of European Operational Research SocietiesLeeds, United Kingdom‌June 2025HAL back‌ to text
34 inproceedings‌‌Y.Yue Su, A.Antoine Désir and‌ A.Axel Parmentier.‌ Learning-based pricing policy for‌‌ substitute itineraries.EURO 2025 - 34th Conference‌ of the Association of‌ European Operational Research Societies‌‌Leeds, United KingdomJune 2025HAL back to‌ text
35 inproceedingsY.‌Yue Su, S.‌‌ N.Sophie N Parragh, N.Nicolas Dupin‌ and J.Jakob Puchinger‌. The Bi-objective Electric‌‌ Autonomous Dial-A-Ride Problem.ROADEFChamps-sur-Marne, FranceFebruary‌ 2025HAL back to‌ text
36 inproceedingsW.‌‌Wenjiao Sun, M.Maxime Ogier and F.‌Frédéric Semet. Considering‌ departure time window on‌‌ generic integrated vehicle routing and driver scheduling problems‌.ROADEF 2025 :‌ 26ème congrès de la‌‌ Société Française de Recherche Opérationnelle et d'Aide à‌ la DécisionChamps-sur-Marne, France‌February 2025HAL back‌‌ to text
37 inproceedingsW.Wenjiao Sun,‌ M.Maxime Ogier and‌ F.Frédéric Semet.‌‌ Considering departure time window on generic integrated vehicle‌ routing and driver scheduling‌ problems.VEROLOG 2025‌‌Trento (Italie), ItalyJune 2025HAL back to‌ text
38 inproceedingsW.‌Wenjiao Sun, M.‌‌Maxime Ogier and F.Frederic Semet. Simultaneous‌ vehicle and driver routing‌ and scheduling with flexible‌‌ departure times.Workshop on Optimization and Algorithms‌ - WOA2025Valparaiso (Chile),‌ ChileDecember 2025HAL‌‌back to text
39 inproceedingsP.Pablo Torrealba-González‌, D.Dominique Feillet‌, M.Maxime Ogier‌‌ and F.Frédéric Semet. A column generation‌ approach to solve the‌ Joint Order Batching and‌‌ Picker Routing Problem with picker congestion.MIP‌ South America 2025 -‌ Mixed Integer Programming Workshop‌‌ South AmericaVina del Mar, ChileDecember 2025‌HAL back to text‌back to text
40‌‌ inproceedingsP.Pablo Torrealba-González, M.Maxime Ogier‌, D.Dominique Feillet‌ and F.Frédéric Semet‌‌. Considering picking discomfort in the picker routing‌ problem.ICCL -‌ EUROMar 2025 - Joint‌‌ International Conference on Computational Logistics and EURO Mini‌ Conference on Maritime Optimization‌ and LogisticsDelft, Netherlands‌‌September 2025HAL back to text

Edition (books,‌ proceedings, special issue of‌ a journal)

41 proceedings‌‌Network Games, Artificial Intelligence,‌ Control and Optimization.NETGCOOP 2024 - 11th‌ International Conference on Network Games, Artificial Intelligence, Control‌ and OptimizationLNCS 15185Lille, FranceSpringerFebruary‌ 2025, 159HALDOI back to text‌

Doctoral dissertations and habilitation theses

42 thesisA.‌Aitor López Sánchez. Distributed, scalable, efficient and‌ fair coordination for agriculture mobile robot fleets.‌Centrale Lille Institut; Universidad Rey Juan Carlos (Madrid)‌September 2025HAL back to text
43 thesis‌F.Francesco Morri. Game-Theoretic and Learning-Based Approaches‌ in Multi-Agent Systems.Université de Lille; Inria‌ & Université de LilleDecember 2025HAL back‌ to text
44 thesisW.Wenjiao Sun.‌ Models and algorithms for integrated vehicle routing and‌ driver scheduling problems.Centrale LilleOctober 2025‌HAL back to text
45 thesisP.Pablo‌ Torrealba-González. Order batching and picker routing in‌ warehouses taking into account human factors.Centrale‌ LilleMarch 2025HALback to text

Reports‌ & preprints

46 miscD.Diego Cattaruzza,‌ M.Martine Labbé, M.Matteo Petris,‌ M.Marius Roland and M.Martin Schmidt.‌ On Multidimensional Disjunctive Inequalities for Chance-Constrained Stochastic Problems‌ with Finite Support.January 2026HAL back‌ to text
47 miscE.Estelle Guenifo Garner‌ Coulibali, F.Frédy Pokou and J.Jules‌ Sadefo Kamdem. Performance Analysis of Regional French‌ Hospitals Before and During the COVID-19 Pandemic.‌March 2025HAL
48 miscF.Francesco Morri‌, H.Hélène Le Cadre, P.Pierre‌ Gruet and L.Luce Brotcorne. Game Theory‌ and Multi-Agent Reinforcement Learning for Zonal Ancillary Markets‌.November 2025HALback to text
49‌ miscF.Francesco Morri, H.Hélène Le‌ Cadre, P.Pierre Gruet and L.Luce‌ Brotcorne. Nonconvex Game and Multi Agent Reinforcement‌ Learning for Zonal Ancillary Markets.June 2025‌HAL
50 miscF.Francesco Morri, H.‌Hélène Le Cadre, P.Pierre Gruet and‌ L.Luce Brotcorne. Supplementary Material for: Game‌ Theory and Multi-Agent Reinforcement Learning for Zonal Ancillary‌ Markets.November 2025HAL
51 miscF.‌Francesco Morri, H.Hélène Le Cadre,‌ D.David Salas and D.Didier Aussel.‌ Steering Noncooperative Games Through Conjecture Design.November‌ 2025HAL back to text
52 miscF.‌Fredy Pokou and H.Hélène Le Cadre.‌ On the Value of Information in Counterinsurgency.‌September 2025HAL
53 miscM.Marius Roland‌, N.Nagisa Sugishita, A.Alexandre Forel‌, Y.Youssouf Emine, R.Ricardo Fukasawa‌ and T.Thibaut Vidal. The Branch-and-Bound Tree‌ Closure.October 2025HAL back to text‌
54 miscP.Pablo Torrealba-González, D.Dominique‌ Feillet, M.Maxime Ogier and F.Frédéric‌ Semet. On the modeling of delays induced‌ by congestion in human-picker-to-part systems.December 2025‌HAL back to textback to text
55‌ miscN.Nathalia Wolf and L.Luce Brotcorne‌. Cloud Sharing for Energy Efficiency: A Bilevel Approach to Incentivize Resource‌ Sharing and Minimize Energy‌ Waste.May 2025‌‌HAL back to text

Other scientific publications

56‌ inproceedingsJ. T.José‌ Tomás Cabezas, V.‌‌Víctor Bucarey, O.Ordonez Fernando and F.‌Frédéric Semet. The‌ Ready-Mixed Concrete Dispatch Problem‌‌ with Uncertain Delivery Times.Mixed Integer Programming‌ Workshop 2025Valparaiso (Chile),‌ ChileDecember 2025HAL‌‌
57 inproceedingsM.Mathis Guckert and H.Hélène‌ Le Cadre. Target‌ Search Using a Swarm‌‌ of UAVs: a Game Formulation with Dynamic Information‌.JRDVA 2025 -‌ Journées Régionales Drones et‌‌ Véhicules AutonomesDunkerque, FranceApril 2025HAL
58‌ miscH.Hélène Le‌ Cadre, M.Mandar‌‌ Datar, M.Mathis Guckert and E.Eitan‌ Altman. Supplementary Material‌ to "Learning Market Equilibria‌‌ Preserving Statistical Privacy Using Performative Prediction".April‌ 2025HAL

12.3 Cited‌ publications

59 articleL.‌‌Luce Brotcorne, F.F. Cirinei, P.‌Patrice Marcotte and G.‌Gilles Savard. A‌‌ Tabu search algorithm for the network pricing problem‌.Comput. Oper. Res.‌39112012,‌‌ 2603--2611URL: https://doi.org/10.1016/j.cor.2012.01.005DOIback to text
60‌ articleL.Luce Brotcorne‌, F.F. Cirinei‌‌, P.Patrice Marcotte and G.Gilles Savard‌. An exact algorithm‌ for the network pricing‌‌ problem.Discret. Optim.822011,‌ 246--258URL: https://doi.org/10.1016/j.disopt.2010.09.003DOI‌back to text
61‌‌ articleA. M.Alysson M. Costa, J.-F.‌Jean-François Cordeau and B.‌Bernard Gendron. Benders,‌‌ metric and cutset inequalities for multicommodity capacitated network‌ design.Comput. Optim.‌ Appl.4232009‌‌, 371--392URL: https://doi.org/10.1007/s10589-007-9122-0DOI back to text‌
62 articleA.Antonio‌ Frangioni and B.Bernard‌‌ Gendron. 0-1 reformulations of the multicommodity capacitated‌ network design problem.‌Discret. Appl. Math.157‌‌62009, 1229--1241URL: https://doi.org/10.1016/j.dam.2008.04.022DOI back‌ to text
63 article‌B.Bernard Gendron and‌‌ F.Frédéric Semet. Formulations and relaxations for‌ a multi-echelon capacitated location-distribution‌ problem.Comput. Oper.‌‌ Res.3652009, 1335--1355URL: https://doi.org/10.1016/j.cor.2008.02.009‌DOI back to text‌
64 inproceedingsG.Géraldine‌‌ Strack, B.Bernard Fortz and F.Fouad‌ Riane. Heuristic Approaches‌ for Integrated Production and‌‌ Distribution Planning.International Conference on Industrial Engineering‌ and Systems ManagementURL:‌ https://hal.archives-ouvertes.fr/hal-01255550back to text‌‌

INOCS - 2025

INOCS - 2025

2025​​​‌Activity reportProject-TeamINOCS﻿​﻿﻿

Keywords

Computer Science​‌﻿﻿ and Digital Science

Other Research Topics and​‌﻿﻿ Application Domains

1 Team members,﻿﻿﻿‌ visitors, external collaborators

Research﻿‌​‌ Scientists

Faculty Members

Post-Doctoral﻿‌​‌ Fellows

PhD Students

Technical﻿​​﻿ Staff

Interns and Apprentices﻿﻿﻿‌

Administrative Assistants

Visiting Scientists

2﻿​​﻿ Overall objectives

2.1 Introduction​​​‌

2.2 Schedule​​​‌ of tasks

3​‌﻿﻿ Research program

3.1 Introduction​​﻿﻿

3.2 Modeling​​﻿﻿ problems with complex structures​​​‌

3.3 Solving problems﻿​﻿﻿ with complex structures

4​​﻿﻿ Application domains

4.1 Energy​​​‌

4.2 Transportation​​﻿﻿ and logistics

4.3 Telecommunications

5﻿﻿﻿‌ Social and environmental responsibility﻿‌​‌

6﻿﻿﻿‌ Highlights of the year﻿‌​‌

6.1 Awards

7 Latest software developments,﻿﻿﻿‌ platforms, open data

7.1﻿‌​‌ Latest software developments

7.1.1﻿​​﻿ GroupTesting

7.1.2 INOCSBox﻿‌​‌

7.1.3 SUMGtfsData

7.1.4​‌﻿﻿ SUMTariffSetting

7.1.5 SUMDesign&Routing​​​‌

7.1.6 SUMImpactAssess

7.1.7﻿‌​‌ SUModp

8 New​​​‌ results

8.1 Large scale﻿‌​‌ complex structure optimization

8.1.1﻿​​﻿ The Branch-and-Bound Tree Closure​​​‌

8.1.2﻿​﻿﻿ A tutorial on Branch-Price-and-Cut​‌﻿﻿ algorithms

8.1.3​​﻿﻿ Solving the Kidney Exchange​​​‌ Problem

8.1.4 Simultaneous vehicle﻿﻿﻿‌ routing and driver scheduling﻿‌​‌ with flexible departure times﻿​​﻿

8.1.5 Agricultural Fleet﻿﻿﻿‌ Vehicle Routing Problem with﻿‌​‌ Implements

8.1.6 Order batching​​﻿﻿ and picker routing problem​​​‌ considering human factors

8.1.7 Nuclear﻿‌​‌ Powerplants Scheduling with Uncertain﻿​​﻿ Outage Durations

8.1.8 Network Design of﻿​﻿﻿ Bike Sharing Systems with​‌﻿﻿ Public Transit Integration.

8.1.9 The Bi-objective Electric​‌﻿﻿ Autonomous Dial-a-Ride Problem

8.2 Bilevel programming and﻿‌​‌ game theory

8.2.1﻿​​﻿ Network games, artificial intelligence,​​​‌ control and optimization

8.2.2 Noncooperative games﻿‌​‌ with prospect theoretic preferences﻿​​﻿

8.2.3 How irrationality shapes﻿​​﻿ Nash equilibria: A prospect-theoretic​​​‌ perspective

8.2.4 Learning﻿﻿﻿‌ market equilibria preserving statistical﻿‌​‌ privacy using performative prediction﻿​​﻿

8.2.5﻿​﻿﻿ Achieving a collective target​‌﻿﻿ through incentives

8.2.6​‌﻿﻿ A generalized potential game​​﻿﻿ approach of UAV swarm​​​‌ coordination for hidden target﻿​﻿﻿ localization

8.2.7﻿​​﻿ Optimal electric vehicle charging​​​‌ with dynamic pricing, customer﻿﻿﻿‌ preferences and power peak﻿‌​‌ reduction

8.2.8 Learning in conjectural﻿﻿﻿‌ Stackelberg games

8.2.9﻿﻿﻿‌ Steering noncooperative games through﻿‌​‌ conjecture designs

8.2.10 Pricing framework​​﻿﻿ for cloud sharing

8.2.11​‌﻿﻿ Spot fare inspection in​​﻿﻿ proof of payment transit​​​‌ systems

8.2.12 Pricing and Trip​‌﻿﻿ Recommendation Problem for integrated​​﻿﻿ shared mobility systems

8.2.13 Game theory and﻿​​﻿ multi-agent reinforcement learning for​​​‌ zonal ancillary markets

8.2.14 Bilevel programming for﻿﻿﻿‌ combinatorial coalition formation

8.2.15 Stackelberg​​​‌ security games

8.3​​​‌ Robust/stochastic programming

8.3.1﻿​﻿﻿ On Multidimensonal Disjunctive Inequalities​‌﻿﻿ for Chance-Constrained Stochastic Problems​​﻿﻿ with Finite Support

8.3.2 Decision Focused​​﻿﻿ Learning for Dynamic Pricing​​​‌ and Inventory Control of﻿​﻿﻿ Substitutable Products

8.3.3﻿​​﻿ Stochastic Bilevel Optimization

8.3.4 Bidding in﻿​​﻿ day-ahead electricity markets

9﻿​​﻿ Bilateral contracts and grants​​​‌ with industry

9.1 Bilateral﻿﻿﻿‌ contracts with industry

Distribution﻿‌​‌ network design - La​​​‌ poste

9.2​​​‌ Exploratory Research Actions and﻿​﻿﻿ Technological Development Actions

9.2.1​‌﻿﻿ Défi Inria-EDF : "Gérer​​﻿﻿ les systèmes électriques de​​​‌ demain"

Optimization methods for﻿​﻿﻿ pricing in the electricity​‌﻿﻿ sector within a regulated​​﻿﻿ competitive environment

Decomposition algorithms for long-term​​​‌ investment problems

Incentive​‌﻿﻿ Design under Bounded Rationality​​﻿﻿

9.2.2﻿​﻿﻿ INRIA Défi with OVH​‌﻿﻿ Cloud (2021-2025)

9.3 Bilateral﻿﻿﻿‌ grants with industry

2025‌Activity reportProject-TeamINOCS

Computer Science‌ and Digital Science

Other Research Topics and‌ Application Domains

1 Team members,‌ visitors, external collaborators

Research‌‌ Scientists

Post-Doctoral‌‌ Fellows

Technical Staff

Interns and Apprentices‌

2 Overall objectives

2.1 Introduction‌

2.2 Schedule‌ of tasks

3‌ Research program

3.1 Introduction

3.2 Modeling problems with complex structures‌

3.3 Solving problems with complex structures

4 Application domains

4.1 Energy‌

4.2 Transportation and logistics

5‌ Social and environmental responsibility‌‌

6‌ Highlights of the year‌‌

7 Latest software developments,‌ platforms, open data

7.1‌‌ Latest software developments

7.1.1 GroupTesting

7.1.2 INOCSBox‌‌

7.1.4‌ SUMTariffSetting

7.1.5 SUMDesign&Routing‌

7.1.7‌‌ SUModp

8 New‌ results

8.1 Large scale‌‌ complex structure optimization

8.1.1 The Branch-and-Bound Tree Closure‌

8.1.2 A tutorial on Branch-Price-and-Cut‌ algorithms

8.1.3 Solving the Kidney Exchange‌ Problem

8.1.4 Simultaneous vehicle‌ routing and driver scheduling‌‌ with flexible departure times

8.1.5 Agricultural Fleet‌ Vehicle Routing Problem with‌‌ Implements

8.1.6 Order batching and picker routing problem‌ considering human factors

8.1.7 Nuclear‌‌ Powerplants Scheduling with Uncertain Outage Durations

8.1.8 Network Design of Bike Sharing Systems with‌ Public Transit Integration.

8.1.9 The Bi-objective Electric‌ Autonomous Dial-a-Ride Problem

8.2 Bilevel programming and‌‌ game theory

8.2.1 Network games, artificial intelligence,‌ control and optimization

8.2.2 Noncooperative games‌‌ with prospect theoretic preferences

8.2.3 How irrationality shapes Nash equilibria: A prospect-theoretic‌ perspective

8.2.4 Learning‌ market equilibria preserving statistical‌‌ privacy using performative prediction

8.2.5 Achieving a collective target‌ through incentives

8.2.6‌ A generalized potential game approach of UAV swarm‌ coordination for hidden target localization

8.2.7 Optimal electric vehicle charging‌ with dynamic pricing, customer‌ preferences and power peak‌‌ reduction

8.2.8 Learning in conjectural‌ Stackelberg games

8.2.9‌ Steering noncooperative games through‌‌ conjecture designs

8.2.10 Pricing framework for cloud sharing

8.2.11‌ Spot fare inspection in proof of payment transit‌ systems

8.2.12 Pricing and Trip‌ Recommendation Problem for integrated shared mobility systems

8.2.13 Game theory and multi-agent reinforcement learning for‌ zonal ancillary markets

8.2.14 Bilevel programming for‌ combinatorial coalition formation

8.2.15 Stackelberg‌ security games

8.3‌ Robust/stochastic programming

8.3.1 On Multidimensonal Disjunctive Inequalities‌ for Chance-Constrained Stochastic Problems with Finite Support

8.3.2 Decision Focused Learning for Dynamic Pricing‌ and Inventory Control of Substitutable Products

8.3.3 Stochastic Bilevel Optimization

8.3.4 Bidding in day-ahead electricity markets

9 Bilateral contracts and grants‌ with industry

9.1 Bilateral‌ contracts with industry

Distribution‌‌ network design - La‌ poste

9.2‌ Exploratory Research Actions and Technological Development Actions

9.2.1‌ Défi Inria-EDF : "Gérer les systèmes électriques de‌ demain"

Optimization methods for pricing in the electricity‌ sector within a regulated competitive environment

Decomposition algorithms for long-term‌ investment problems

Incentive‌ Design under Bounded Rationality

9.2.2 INRIA Défi with OVH‌ Cloud (2021-2025)

9.3 Bilateral‌ grants with industry

SAMOA,‌‌ Stackelberg Games for Flexibility (Dis)Aggregation

JEDI, game theory for‌ the electricity system decarbonization‌‌

Integrated transit system with‌ Electric RIDE-sharing and Mobility‌‌ Pickup Stations in smart grid (ERIDE-MoPS)

Grant from AID

PGMO IROE C‌ project TROMAT (2025-2026)

10 Partnerships and cooperations

10.1‌ International initiatives

10.1.1 Inria associate team not involved‌ in an IIL or an international program

GALANGAL‌

10.1.2 STIC/MATH/CLIMAT AmSud‌ projects

10.1.3 Participation in other International Programs