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

8.1.1 Distribution network design

Retailers which deliver products directly to their customer locations often rely on Logistics Service Intermediaries (LSI) for order management, warehousing, transportation and distribution services. Usually, the LSI acts as a shipper and subcontracts the transportation to carriers for long-haul and last-mile delivery services. All agents interact and are connected through cross-docking facilities. As the demand from customers may vary significantly over time, the shipper's requirements for transportation evolve at the tactical level. This creates opportunities for the shipper to take advantage of medium-term contracts with the carriers at prices lower than those offered by the spot market. In 14, we focus on the tactical design, through dynamic contracts, of a suitable network of cross-docking facilities and related transportation capacities (belonging to different carriers) to reduce the shipper's operational costs. In this article, we propose a Mixed Integer Linear Programming (MILP) formulation for the multi-period planning problem with minimum purchase commitment contracts faced by the shipper. We propose exact and heuristic decomposition methods for the the model, respectively, based on combinatorial Benders cuts and on relax-and-repair approaches. The performance of these algorithms is experimentally compared to that of commercial solvers. The numerical results show that our methods perform comparatively well for the solution of large size instances and brings economic benefits to the shipper. This work was motivated by a real-life case which is described in 39.

8.1.2 Home chemotherapy delivery: an integrated production scheduling and multi-trip vehicle routing problem.

Home chemotherapy systems allow the administration of cancer treatments at a patient's residence, avoiding an admission to inpatient care facilities. This innovative health care model is interesting both economically and on a human level. It also raises several logistical challenges. In the paper 11, we focus on one of the optimization problems arising in the context of home chemotherapy services, where a complex scheduling problem underlies the operational planning process. Indeed, some injectable chemotherapy drugs may remain stable only during a few hours after being produced. Consequently, their production has to be carefully scheduled jointly with their administration, which takes place at the patients homes during a predefined time window. This gives rise to an integrated production scheduling and vehicle routing problem, that we address using a large neighborhood search approach. Production and administration sequences are iteratively modified, while a linear program is used to determine optimal production and administration start times for the candidate sequences. We analyze the impact of the linear program and establish that it is a crucial component of the proposed method. We assess the performance of the proposed method by comparing its solutions with those obtained through a compact mathematical formulation. We then provide insights about the cost of taking into consideration time-related aspects of the problem, i.e., integrated planning horizons, drug stability times, and administration time windows.

8.1.3 A review of optimization of human-aware manufacturing and logistics systems

Historically, Operations Research (OR) discipline has mainly been focusing on economic concerns. Since the early 2000s, human considerations are gaining increasing attention, pushed by the growing societal concerns of sustainable development on the same terms as the economic and ecological ones. In this work, we aim at reviewing the efforts dedicated by the OR community to the integration of human factors into logistics and manufacturing systems. In a first survey 20, a focus is put on the modeling and solution approaches used to consider human characteristics, their practical relevance, and the complexity induced by their integration within optimization models. The material presented in this work has been retrieved through a semi-systematic search of the literature. Then, a comprehensive analysis of the retrieved corpus is carried out to map the related literature by class of problems encountered in logistics and manufacturing. These include warehousing, vehicle routing, scheduling, production planning, and workforce scheduling and management. We investigate the mathematical programming techniques used to integrate human factors into optimization models. Finally, a number of gaps in the literature are identified, and new suggestions on how to suitably integrate human factors in OR problems encountered in logistics and manufacturing systems are discussed. In a second survey 21, a focus is put on the models proposed to represent and quantify human characteristics, their practical relevance, and the complexity induced by their integration with mathematical optimization models. In this paper, the techniques used in the OR literature to represent the human considerations encountered in logistics and manufacturing systems are surveyed. The existing Human-Aware Models (HAM) are classified and reviewed by domain. Particular attention is paid to the field validity of each method, its relevance to specific use cases, the required data collection, and its usability within mathematical optimization models. Since the surveyed HAMs rely on concepts originating from different related scientific disciplines (e.g., ergonomics, occupational medicine), a brief introduction of these fields of study is proposed together with a work of contextualization that is carried out during the analysis.

8.1.4 Commodity constrained split delivery vehicle routing problems

We propose a survey 15 on vehicle routing problems with multiple commodities. In most routing problems, only one commodity is explicitly considered. This may be due to the fact that, indeed, a single commodity is involved, or multiple commodities are transported, but they are aggregated and modeled as a single commodity, as no specific requirement imposes their explicit consideration. However, there exist cases in which this aggregation is not possible due to the characteristics of the commodities or to the fact that it would lead to sub-optimal routing plans. The survey focuses on the analysis of the settings of the problems and the features of the commodities that require explicit consideration of disaggregated commodities in routing problems. We show that problem settings are inherently different with respect to the single commodity problems, and this has a consequence on both models and solution approaches, which cannot be straightforwardly adapted from the single commodity cases. We propose a classification of the routing problems with multiple commodities and discuss the motivations that force considering the presence of multiple commodities explicitly. Specifically, we focus on the modeling perspective by proposing a general formulation for routing problems with multiple commodities and showing how this formulation can be adapted to the different features that characterize the problem classes discussed in the survey. Also, for each major class of problems, promising future research directions are discussed by analyzing what has been studied in the current literature and focusing on challenging topics not covered yet.

We address two specific routing problems with multiple commodities. The Commodity constrained Split Delivery Vehicle Routing Problem (C-SDVRP) is a routing problem where customer demands are composed of multiple commodities. A fleet of capacitated vehicles must serve customer demands in a way that minimizes the total routing costs. Vehicles can transport any set of commodities and customers are allowed to be visited multiple times. However, the demand for a single commodity must be delivered by one vehicle only. In this work, we developed a heuristic with a performance guarantee to solve the C-SDVRP. The proposed heuristic is based on a set covering formulation, where the exponentially-many variables correspond to routes. First, a subset of the variables is obtained by solving the linear relaxation of the formulation by means of a column generation approach which embeds a new pricing heuristic aimed to reduce the computational time. Solving the linear relaxation gives a valid lower bound used as a performance guarantee for the heuristic. Then, we devise a restricted master heuristic to provide good upper bounds: the formulation is restricted to the subset of variables found so far and solved as an integer program with a commercial solver. A local search based on a mathematical programming operator is applied to improve the solution. We test the heuristic algorithm on benchmark instances from the literature. The comparison with the state-of-the-art heuristics for solving the C-SDVRP shows that our approach significantly improves the solution time, while keeping a comparable solution quality and improving some best-known solutions. In addition, our approach is able to solve large instances with 100 customers and six commodities, and also provides very good quality lower bounds. Furthermore, an instance of the C-SDVRP can be transformed into a CVRP instance by simply duplicating each customer as many times as the requested commodities and by assigning as demand the demand of the single commodity. Hence, we compare heuristics for the C-SDVRP against the state-of-the-art heuristic for the Capacitated Vehicle Routing Problem (CVRP). The latter approach revealed to have the best performance. However, our approach provides solutions of comparable quality and has the interest of providing a performance guarantee 19.

An extended variant of the C-SDVRP is the Multi-Commodity two-echelon Distribution Problem (MC2DP). In the MC2DP, multiple commodities are distributed in a two-echelon distribution system involving suppliers, distribution centres and customers. Each supplier may provide different commodities and each customer may request several commodities as well. In the first echelon, capacitated vehicles perform direct trips to transport the commodities from the suppliers to the distribution centres for consolidation purposes. In the second echelon, each distribution centre owns a fleet of capacitated vehicles to deliver the commodities to the customers through multi-stop routes. Commodities are compatible, i.e., they can be mixed in the vehicles. Finally, customer requests can be split by commodities, that is, a customer can be visited by several vehicles, but the total amount of each commodity has to be delivered by a single vehicle. The aim of the MC2DP is to minimize the total transportation cost to satisfy customer demands. In the paper 18, we propose a set covering formulation for the MC2DP where the exponential number of variables relates to the routes in the delivery echelon. We develop a Branch-Price-and-Cut algorithm (BPC) to solve the problem. The pricing problem results in solving an Elementary Shortest Path Problem with Resource Constraints (ESPPRC) per distribution centre. We tackle the ESPPRC with a label setting dynamic programming algorithm which incorporates ng-path relaxation and a bidirectional labelling search. Pricing heuristics are invoked to speed up the procedure. In addition, the formulation is strengthened by integrating capacity cuts and two families of valid inequalities specific for the multiple commodities aspect of the problem. Our approach solves to optimality 439 over the 736 benchmark instances from the literature. The optimality gap of the unsolved instances is 2.1%, on average.

8.1.5 The joint order batching and picker routing problem

We address the Joint Order Batching and Picker Routing Problem (JOBPRP). Given a set of orders to collect, each order being composed of several items located in a warehouse, the JOBPRP consists in batching orders into capacitated trolleys such that the total travelled distance to collect all the items of the orders is minimized. We are interested in algorithms based on column generation. A bottleneck of such approaches is to efficiently solve the pricing problem, that is a profitable order picking problem where all products of an order must be collected together in the same tour and a price is gained for collecting an order. At the core of this pricing problem lies a profitable Traveling Salesman Problem (TSP) in a rectangular warehouse. Dynamic Programming (DP) approaches can solve efficiently this TSP for such layouts. In the work 33, we extend the DP approach to the profitable variant. The directed acyclic graph underlying the DP can be used to provide a powerful Mixed Integer Programming (MIP) formulation where the order requirements (all products of an order must be collected together) can be taken into account with linking constraints. Such MIP formulation has been studied for the special case of warehouses with a single bloc of aisles. We generalize to the case with several blocks, and propose state space restrictions of the DP to heuristically solve the pricing problem. The proposed approach is validated on instances from the literature.

In the context of warehosue operations, picker-to-part systems are operational setups in which operators, commmonly called pickers, navigate around the warehouse collecting all the necessary products to prepare customer orders. In the Operations Research scope, a common assumption is to consider a complete independent operation scenario for each picker, neglecting potential interactions between pickers. Nowadays, given the changes on the behavior of customers it is common to see a large number of pickers operating simultaneously. This frame can lead to undesirable situations in which congestion appears. Congestion occurs when two or more pickers compete for the use of a shared space, delaying operational times and impacting overall performance. In the work 31, we propose a mathematical and computational approach to estimate the delay produced by congestion on the total operation times. We analyze in 38 the delay in terms of the main factors and different storage policies and evaluate the effect of approximating it by using a time discretization. Finally, in 30 we present a column generation based solving procedure to optimize order batching and picker routing decisions, explicitly considering the impact of congestion on the total completion times.

8.1.6 A column generation based heuristic for the integrated vehicle routing and driver scheduling problem

Integrated vehicle Routing and driver Scheduling Problems (RSPs) consist in simultaneously planning routes for vehicles and drivers' schedules. Usually, classical routing problems assume that each vehicle is associated with a single driver throughout its entire route. However, when planning over a large horizon, vehicles can be used without interruption over the whole horizon, while drivers have to rest. Hence, in order to better use the vehicles, in this work, we consider that each of them can be driven by different drivers during the planning horizon. RSPs have been investigated in different transportation applications, including airline industry, railway, trucks, buses. Although these transportation applications have different characteristics, they all share several main similarities. We denote by task a trip from two locations with predetermined departure and arrival times. A task has to be performed by one or several combined vehicles, and one driver has to be driving. The combined vehicle case typically appears for trains and trucks with trailers. Note that drivers can also be passengers on the vehicle. Given a set of tasks, the aim is to plan the sequence of tasks to be performed by each vehicle and each driver such that each task is performed by the required number of vehicles and at least one driver. We propose in 37 a generic compact formulation for these different applications. To handle large-size instances in a reasonable amount of time, we decompose the integrated RSP into two subproblems (1) a driver scheduling problem, and (2) a vehicle routing problem. We then solve the RSP with a two phases heuristic : first, we solve the driver scheduling subproblem, and then, knowing the driver schedules, we solve the vehicle routing sub-problem. We evaluated the quality of the solution with respect to optimal solutions for small-size instances. The two-phases approach provides very good results in general, except for some instances.

8.1.7 Fair and efficient cooperative vehicle routing

In addition to reducing their own consumption, communities often allow the socialization of total costs among their members. In this setting, a recurrent question is related to the design of fair cost allocation mechanisms. In 17, a cooperative is modeled as a business entity whose primary purpose is to provide benefits, services, and goods to its members, who both own and democratically control it. In the context of a cooperative, a fleet typically consists of vehicles owned by self-concerned, individually rational owners who prioritize their own efficiency and the fairness of the system. This fairness refers to how their individual gain aligns with the gain of others. In this paper, we focus on the routing of such cooperative fleets. If we consider only the efficiency of the fleet in terms of minimizing its total cost, the problem studied corresponds to the Multiple Traveling Salesman Problem (MTSP). However, our interest lies in finding both efficient and fair solutions, so we propose two new variants of this problem that integrate and maximize the egalitarian and elitist social welfare. Additionally, to enhance the balance between fleet efficiency and fairness, we propose the systematic elitist and systematic egalitarian social welfare optimization algorithm. Through simulation results, we observe a wide diversity of routes depending on the approach considered. Therefore, a cooperative may choose a model that best balances criteria of efficiency maximization and fairness for its fleet based on its specific requirements.

In 22 we propose a model and a solution method for the Dynamic Vehicle Routing Problem with Fair Profits and Time Windows (DVRP-FPTW). The goal is to dynamically optimize routes for a fleet of vehicles to perform tasks within assigned time windows, emphasizing fair and efficient solutions. The DVRP-FPTW accounts for unforeseen events, such as task changes or vehicle breakdowns, and ensures compliance with task demands, vehicle capacities, and autonomies. The proposed model incorporates mandatory and optional tasks, including optional tasks in operational vehicle routes when it does not affect vehicle profits. Incorporating asynchronous and distributed column generation heuristics, the proposed multi-agent architecture for the DVRP-FPTW dynamically adapts to unforeseen events. Systematic Egalitarian Social Welfare Optimization (SESWO) is used to iteratively maximize the profit of the least profitable vehicle, prioritizing fairness across the fleet in the face of unforeseen events. This improves upon existing dynamic and multi-period VRP models that rely on prior knowledge of demand changes. Our approach allows vehicle agents to maintain privacy while sharing minimal local data with a fleet coordinator agent. We propose publicly available benchmark instances for both static and dynamic VRP FPTW. Simulation results demonstrate the effectiveness of our VRP-FPTW model and our multi-agent system solution approach in coordinating large, dynamically evolving, cooperative autonomous fleets fairly and efficiently in near real time.

8.1.8 $\lambda$-cent-dians and generalized-center for network design

In this work 43, we extend the notions of $\lambda$-cent-dian and generalized-center from Facility Location Theory to the more intricate domain of network design. Our focus is on the task of designing a sub-network within a given underlying network while adhering to a budget constraint. This sub-network is intended to efficiently serve a collection of origin/destination pairs of demand rather than individual points. The $\lambda$-cent-dian problem studies the balance between efficiency and equity. We investigate the properties of the $\lambda$-cent-dian and generalized-center solution networks under the lens of equity, efficiency, and Pareto-optimality. We provide a mathematical formulation for $\lambda \ge 0$ and discuss the bilevel structure of this problem for $\lambda >1$. Furthermore, we describe a procedure to obtain a complete parametrization of the Pareto-optimality set based on solving two mixed integer linear formulations by introducing the concept of maximum $\lambda$-cent-dian. We evaluate the quality of the different solution concepts using some inequality measures. Finally, for $\lambda \in [0,1]$, we study the implementation of a Benders decomposition method to solve it at scale.

8.2.1 On stability of nonlinear armsbuilding security games

We formulate a security game in a context of mixed armament acquisition, involving a finite set of Nations in strategic relationship, with utility functions which are nonlinear and non-differentiable on the boundary of their sets of definition 16, 50. Since we want to study the long-term effect of the Nations' investment in nuclear weapons, we focus on the steady-state analysis of the game. This requires us to extend the classical results from Rosen on compact-concave games, to unbounded concave games, relying on the uniform coercivity property of the Nations' utility functions. In addition, we prove the existence and uniqueness, under mild assumptions, of an interior point Nash Equilibrium solution of the game. To learn the interior point Nash Equilibrium, we provide a decentralized proximal point-type algorithm, and derive a closed form expression of its convergence rate. Simulations are performed in case of a duopoly, illustrating the utility improvement and stabilizing effects of nuclear armaments, by comparison with the conventional-only setting. We subsequently generalize this result to an arbitrarily large, but finite, number of Nations. The problem is extended further by allowing arms trades between Nations, split into suppliers on the one side, and recipients-interpreted as opponents-on the other side. It is widely recognized that arms trades can have negative externalities on the supplier Nations' security, and there are benefits to cooperate. Forming an export coalition enables the socialization of the operational costs among the coalition members and reallocation according to an ex-post cost allocation mechanism, while ensuring the maximization of the sum of the coalition members' security functions. For a given coalition structure, we develop a partial equilibrium model of the international arms market. We prove the existence and uniqueness of a market equilibrium in arms trade prices and supply. Finally, we derive conditions on the exports, arms trade prices, and cost allocation mechanism guaranteeing the stability of the export coalition, thus highlighting the viability of a common defense policy for the export coalition.

8.2.2 Noncooperative games with prospect theoretic preferences

We study noncooperative stochastic games, where agents display irrational behaviors in response to underlying risk factors in 44. 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 non-convex non-smooth 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 involving strategic end users exposed to risk.

Prospect-theoretic games are also studied in the context of the design of optimal sanctions in 28, 23.

8.2.3 A reverse Stackelberg model for demand response in local energy markets

In an era where renewable energy resources are increasingly integrated into our power systems, and consumer-centric approaches gain traction, local energy markets emerge as a pivotal mechanism for empowering prosumers. In 49, we present a novel bilevel optimization model that uniquely blends the dynamics of peer–to–peer energy markets with the physical realities of power distribution networks. The innovation stems from introducing a tariff design approach based on affine functions to shape prosumer behavior towards operationally efficient and secure energy exchanges. This is critical as previous market designs often overlooked the physical constraints of power flows, leading to potential risks in voltage regulation and economic efficiency. The lower level of the model encapsulates the interactions among prosumers in a Generalized Nash Equilibrium Problem (GNEP), modeling active and reactive power injections of prosumers. The upper level, representing the role of the distribution system operator, strategically computes tariffs to steer the market to an operationally efficient equilibrium. The paper relies on the classical Nikaido–Isoda (NI) reformulation to characterize the GNEP, a key aspect in leveraging a proof of strong stability of the lower–level solution. Computational experiments on various IEEE test feeder instances reveal the model’s capacity to efficiently align prosumer behavior with operational objectives, utilizing only the tariff information, thereby simplifying the decision-making process in complex distribution systems.

8.2.4 Forecast trading as a means to reach social optimum on a peer-to-peer market

In 29, we investigate the coupling between a peer-to-peer (P2P) electricity market and a forecast market to alleviate the uncertainty faced by prosumers regarding their Renewable Energy Sources (RES) generation. The work generalizes the analysis from Gaussian-distributed RES production to arbitrary distributions. The P2P trading is modeled as a generalized Nash equilibrium problem, where prosumers trade energy in a decentralized manner. Each agent has the option to purchase a forecast on the forecast market before trading on the electricity market. We establish conditions on arbitrary Probability Density Functions (PDFs) under which the prosumers have incentives to purchase forecasts on the forecast market. Connected with the previous results, this allows us to prove the economic efficiency of the P2P electricity market, i.e., that a social optimum can be reached among the prosumers.

8.3.1 Learning in Stackelberg games with application to strategic bidding in the electricity market

We formulate a two-stage electricity market involving conventional and renewable producers strategically bidding in the day-ahead market, to maximize their profits while anticipating the market clearing performed by an Independent System Operator (ISO), as a multi-leader single follower Stackelberg game 27. In this game, producers are interpreted as leaders, while the ISO acts as a follower. To compute an equilibrium, the classical approach is to cast the Stackelberg game as a Generalized Nash Game (GNG), replacing the ISO's optimization problem by its KKT constraints. To solve this reformulated problem, we can either rely on the Gauss-Seidel Best-Response method (GS-BR), or, on the Alternating Direction Method of Multipliers (ADMM). However, both approaches are implemented in a centralized setting since they require the existence of a coordinator which keeps track of the history of agents' strategies and sequential updates, or, is responsible for the Lagrange multiplier updates following the augmented Lagrangian. To allow the agents to selfishly optimize their utility functions in a decentralized setting, we introduce a variant of an actor-critic Multi-Agent deep Reinforcement Learning (MARL) algorithm with provable convergence. Our algorithm is innovative in that it allows different levels of coordination among the actors and the critic, thus capturing different information structures of the Stackelberg game. We conclude this work by comparing GS-BR and ADMM, both used as benchmark, to the MARL, on a dataset from the French electricity market, relying on metrics such as the efficiency loss and the accuracy of the solution.

8.3.2 Winning the 2023 CityLearn challenge: a community-based hierarchical energy systems coordiantion algorithm

The effective management and control of building energy systems are crucial for reducing the energy consumption peak loads, CO${}_2$ emissions, and ensuring the stability of the power grid, while maintaining optimal comfort levels within buildings. The difficulty to accommodate this trade-off is amplified by dynamic environmental conditions and the need for scalable solutions that can adapt across various building types and geographic locations. Acknowledging the importance of this problem, NeurIPS conference hosted since 2020 the CityLearn control challenge to foster the design of innovative solutions in building energy management. Participants were tasked with developing strategies that not only enhance energy efficiency but also prioritize sustainability and occupant comfort. In 24, we introduce the Community-based Hierarchical Energy Systems Coordination Algorithm (CHESCA), the winning approach of the 2023 edition. We rely on a hierarchical approach adaptable to an arbitrary number of buildings, first optimizing building-level metrics individually, and later refining these through a central community-level controller to improve grid-related metrics. Compared to the other high-ranked competitors, our approach demonstrated fast inference capabilities like learning-based methods, while offering a better interpretability and a superior generalization capabilities with minimal data requirements. This paper details our approach, supported by comprehensive experimental results and ablation studies.

8.3.3 Exact and heuristic solution techniques for mixed-integer quantile minimization problems

We consider mixed-integer linear quantile minimization problems that yield large-scale problems that are very hard to solve for real-world instances. We motivate the study of this problem class by two important realworld problems: a maintenance planning problem for electricity networks and a quantile-based variant of the classic portfolio optimization problem. For these problems, we develop valid inequalities and present an overlapping alternating direction method. Moreover, we discuss an adaptive scenario clustering method for which we prove that it terminates after a finite number of iterations with a global optimal solution. We study the computational impact of all presented techniques and finally show that their combination leads to an overall method that can solve the maintenance planning problem on large-scale real-world instances provided by the EURO/ROADEF challenge 2020 and that they also lead to significant improvements when solving a quantile-version of the classic portfolio optimization problem 13.

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.

Distribution network design - La poste

Participants: Gaël Guillot, Diego Cattaruzza, Frédéric Semet, Olivier Beaude (EDF).

(January to December 2024). A PhD thesis related to this project will start in 2025.

10.1.1 Associate teams in the framework of an Inria international lab or in the framework of an Inria international program

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

10.1.3 STIC/MATH/CLIMAT AmSud projects

10.2.1 Visits of international scientists

10.3.1 Horizon Europe

10.4.1 ANR

ANR project AGIRE (2020-2024): "Decision system for smart management of resources in warehouses" in collaboration with Ecole des Mines de Saint-Etienne (Gardanne), Université Gustave Eiffel (Champs-sur-Marne), HappyChic (Tourcoing). This project addresses human resources management in warehouses which supply either sale points or final consumers. Nowadays, such warehouses are under pressure. This is mainly due to the no inventory policy at the sale points and to the constant growth of e-commerce sales in France and Europe. In terms of logistics, this translates into an increasing number of parcels to prepare and to ship to satisfy an order, which is known typically a few hours before. Moreover, the total number of products to be packed varies very significantly from day-to-day by a factor of at least 3. The novelty of the project is twofold: (1) The human factor is explicitly be taken into account. It is integrated in the mathematical models and algorithms that are developed for the project. The aim is to improve the quality of employees' work ensuring the efficiency of the logistic system; (2) Problems at different decision levels are integrated and tackled jointly. At the tactical level, the main issues are workload smoothing and the management of the storage zone. At operational level, the major issues concern the rearrangement of the picking zone, the picking tours, and the dynamic reorganization of activities to manage uncertainties.

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.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• 29th POC seminar - Combinatorial Optimization and Supply-Chain: Frédéric Semet.
• EURO 2024: Hélène Le Cadre , Fredy Vale Manuel Pokou .

11.1.5 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.
• NETGCOOP: Hélène Le Cadre , member of the steering committee.

11.1.6 Scientific expertise

• ANID, Chile Evaluation Group: Luce Brotcorne.
• Scientific committee of France-Netherlands exchange program: Luce Brotcorne - Member.
• Evaluation committee for INRIA/MITACS exchange program: Luce Brotcorne - Member.
• Evaluation committee COST GTRI: Luce Brotcorne - Member.
• NSERC evaluation group 1509: Bernard Fortz.
• IVADO International Advisory Committee, Canada: Martine Labbé - Member.
• Scientific orientation committee of the Interuniversity Centre on Entreprise Networks, Transportation and Logistics (CIRRELT), Canada: Bernard Fortz, Frédéric Semet - Members.
• Research Council of Norway, Norway: Frédéric Semet - Panel member.
• EURO Doctoral Dissertation Award - Association of the European Operational Research Societies (EURO): Frédéric Semet - Member.
• PE6 panel for the evaluation of ERC Consolidator grants: Martine Labbé - Member.

11.1.7 Research administration

• Vice–president 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 OPTIMA scientific council: Diego Cattaruzza.

11.2.1 Teaching

• Master: Bernard Fortz, Operations Reserach and applications, 30hrs, M1, University of Mons (Charleroi campus), Belgium.
• Master: Bernard Fortz, Continuous Optimization, 24hrs, M1 & M2, Université libre de Bruxelles, Belgium.
• Master: Frédéric Semet, Supply Chain Management, 30hrs, M2, Centrale Lille.
• Master: Frédéric Semet, Operations Research, 28hrs, M2, Centrale Lille.
• Master: Luce Brotcorne, Optimisation, 14hrs, M1, Polytech Lille.
• Master: Luce Brotcorne, Recherche opérationnelle, 16hrs, M1 apprentissage, Polytech Lille.
• Master: Diego Cattaruzza, Frédéric Semet, Prescriptive analytics and optimization, 64hrs, M1, Centrale Lille.
• Master: Diego Cattaruzza, Maxime Ogier, Frédéric Semet, Object-Oriented Programming, 32hrs, M1, Centrale Lille.
• Master: Maxime Ogier, Operations Research, 24hrs, M1, Centrale Lille.
• Licence: Maxime Ogier, Object-Oriented Programming, 80hrs, L3, Centrale Lille.
• Licence: Frédéric Semet, Advanced programming and Complexity, 24hrs, L3, Centrale Lille.
• Licence: Maxime Ogier, Object-Oriented Programming, 96hrs, L2, Centrale Lille.
• Licence: Bernard Fortz, Algorithmic 1, 12hrs, L1, Université libre de Bruxelles, Belgium.
• Licence: Bernard Fortz, Algorithmic and Operations Research, 24hrs, L3, Université libre de Bruxelles, Belgium.
• Licence: Yue Su , Introduction to development, 27hrs, L1, Université de Lille, France.
• Licence: Yue Su , Introduction of database and SQL, 64hrs, L1, Université de Lille, France.
• Licence: Yue Su , New paradigms of database, 23hrs, L3, Université de Lille, France.
• Licence: Yue Su , Implementation the needs of clients (R1.01) - Dédoublement, 3hrs, L1, Université de Lille, France.
• Licence: Yue Su , Comparison algorithms, 5hrs, L1, Université de Lille, France.
• Licence: Yue Su , Creation of database, 8hrs, L1, Université de Lille, France.

11.2.2 Supervision

• PhD completed: Haider Ali, Optimal techniques for charging autonomous electric vehicles with renewable energy sources, December 2024, Luce Brotcorne, Bruno François (Centrale Lille).
• PhD completed: Clément Legrand, Intégration de machine learning pour la résolution de MO-VRPTWs, October 2024, Diego Cattaruzza, Marie-Eleonore Kessaci (Polytech Lile), Laetitia Jourdan (University of Lille).
• PhD completed: Thibault Prunet, Models and algorithm for tactical problems in warehouses with human factor considerations, January 2024, Nabil Absi (Mines de Saint Etienne), Valeria Borodin (Mines de Saint Etienne), Diego Cattaruzza.
• PhD in progress: Pablo Torrealba Gonzalez, Batching and picker routing problems in warehouses taking into account human factors, from February 2021, Dominique Feillet (Mines de Saint Etienne), Maxime Ogier, Frédéric Semet.
• PhD in progress: Wenjiao Sun, Exact methods for integrated routing and scheduling problems, from October 2021, Maxime Ogier, Frédéric Semet.
• PhD in progress: Francesco Morri, Game-theoretic learning in intelligent marketplaces, from October 2022, Hélène Le Cadre , Luce Brotcorne .
• PhD in progress: Juan Sepulveda, New optimization models and algorithms to represent energy exchanges in local energy communities, from December 2021, Hélène Le Cadre , Luce Brotcorne .
• PhD in progress: Cristian Aguayo, Models and algorithms for energy disptaching in local energy communities, from July 2021, Bernard Fortz.
• PhD in progress: Tifaout Almeftah, Models and algorithms for group testing, from December 2021, Diego Cattaruzza, Martine Labbé, Frédéric Semet.
• PhD in progress: Hugo Callebaut, Segment routing optimization, from September 2022, Bernard Fortz.
• PhD in progress: Aitor Lopez Sanchez, Distributed, scalable, and efficient fleet coordination for agriculture mobile robots, from September 2022, 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: Salma Janati, Integration of forecasting methods into optimization algorithms: application to urban logistics, from October 2024, Luce Brotcorne, Frédéric Semet.

11.2.3 Juries

• Thibault Prunet, PhD, Mines de Saint-Etienne, Human-Aware Optimization of Integrated Order Picking Decisions in Warehousing Logistics: Maxime Ogier - Reviewer.
• Louise Sadoine, PhD, Mons University, Belgium, Noncooperative Game Theory for Resource Planning in Communities: Hélène Le Cadre - Reviewer.

International journals

• 11 articleY.Yasemin Arda, D.Diego Cattaruzza, V.Véronique François and M.Maxime Ogier. Home chemotherapy delivery: An integrated production scheduling and multi-trip vehicle routing problem.European Journal of Operational ResearchMarch 2024HALDOIback to text
• 12 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
• 13 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 Computing2024. In press. HALback to text
• 14 articleC.Christian Clavijo López, Y.Yves Crama, T.Thierry Pironet and F.Frédéric Semet. Multi-period distribution networks with purchase commitment contracts.European Journal of Operational Research31222024, 556-572HALDOIback to text
• 15 articleW.Wenjuan Gu, C.Claudia Archetti, D.Diego Cattaruzza, M.Maxime Ogier, F.Frédéric Semet and M. G.M. Grazia Speranza. Vehicle routing problems with multiple commodities: A survey.European Journal of Operational Research31712024, 1-15HALDOIback to text
• 16 articleJ.Jean Le Hénaff, J.Jed Boufaied and H.Hélène Le Cadre. On Stability of Nonlinear Armsbuilding Security Games.Dynamic Games and Applications2024. In press. HALback to text
• 17 articleA.Aitor López Sánchez, M.Marin Lujak, F.Frederic Semet and H.Holger Billhardt. How to achieve fair and efficient cooperative vehicle routing?AI Communications3722024, 223-245HALDOIback to text
• 18 articleM.Matteo Petris, C.Claudia Archetti, D.Diego Cattaruzza, M.Maxime Ogier and F.Frédéric Semet. A Branch-Price-and-Cut algorithm for the Multi-Commodity two-echelon Distribution Problem.EURO Journal on Transportation and Logistics132024, 100139HALDOIback to text
• 19 articleM.Matteo Petris, C.Claudia Archetti, D.Diego Cattaruzza, M.Maxime Ogier and F.Frédéric Semet. A heuristic with a performance guarantee for the Commodity constrained Split Delivery Vehicle Routing Problem.Networks844July 2024, 446-464HALDOIback to text
• 20 articleT.Thibault Prunet, N.Nabil Absi, V.Valeria Borodin and D.Diego Cattaruzza. Optimization of Human-Aware Manufacturing and Logistics Systems: A Comprehensive Review of Modeling Approaches and Applications.EURO Journal on Transportation and Logistics2024, 100136HALDOIback to text
• 21 articleT.Thibault Prunet, N.Nabil Absi, V.Valeria Borodin and D.Diego Cattaruzza. Optimization of Human-Aware Manufacturing and Logistics Systems: A Survey on the Modeling Frameworks of Human Aspects.EURO Journal on Transportation and Logistics2024, 100137In press. HALDOIback to text
• 22 articleA. L.Aitor López Sánchez, M.Marin Lujak, F.Frédéric Semet and H.Holger Billhardt. Dynamic, fair, and efficient routing for cooperative autonomous vehicle fleets.Expert Systems with Applications251October 2024, 123964HALDOIback to text

Invited conferences

• 23 inproceedingsF.Fredy Pokou, M.Marta Fochesato and H.Hélène Le Cadre. Gas Trading with Tariff Versus Sanction as a Networked Game.33rd EURO ConferenceCopenhagen, DenmarkMarch 2024HALback to text

International peer-reviewed conferences

• 24 inproceedingsA. I.Andoni I Garmendia, F.Francesco Morri, Q.Quentin Cappart and H.Hélène Le Cadre. Winning the 2023 CityLearn Challenge: a Community-based Hierarchical Energy Systems Coordination Algorithm.27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCESantiago de Compostela, SpainOctober 2024HALback to text
• 25 inproceedingsC.Clément Legrand, D.Diego Cattaruzza, L.Laetitia Jourdan and M.-E.Marie-Eléonore Kessaci. Investigation of the Benefit of Extracting Patterns from Local Optima to Solve a Bi-objective VRPTW.Metaheuristics International ConferenceMIC 2024 - Metaheuristics International Conference14753Lecture Notes in Computer ScienceLorient, FranceSpringer Nature SwitzerlandJune 2024, 62-77HALDOI
• 26 inproceedingsC.Clément Legrand, D.Diego Cattaruzza, L.Laetitia Jourdan and M.-E.Marie-Eléonore Kessaci. Solution-based Knowledge Discovery for Multi-objective Optimization.Parallel Problem Solving from Nature – PPSN XVIIIPPSN 2024Hagenberg, AustriaSeptember 2024HAL
• 27 inproceedingsF.Francesco Morri, H.Hélène Le Cadre, L.Luce Brotcorne and P.Pierre Gruet. Learning in Stackelberg Games with Application to Strategic Bidding in the Electricity Market.EEM24Istanbul, TurkeyIEEEJune 2024, 1-7HALDOIback to text
• 28 inproceedingsF.Fredy Pokou, M.Marta Fochesato and H. L.Hélène Le Cadre. Tariff Versus Sanction Under Bounded Rationality.NETGCOOP 2024Lille, FranceJune 2024HALback to text
• 29 inproceedingsI.Ilia Shilov, H.Hélène Le Cadre, A.Ana Bušić, A.Anibal Sanjab and P.Pierre Pinson. Forecast Trading as a Means to Reach Social Optimum on a Peer-to-Peer Market.NETGCOOP 2024Netgcoop 2024 - 11th International Conference on Network Games, Control and OptimizationLille, FranceOctober 2024HALback to text
• 30 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.Odysseus 2024-the Ninth International Workshop on Freight Transportation and LogisticsSeville, SpainMay 2024HALback to text
• 31 inproceedingsP.Pablo Torrealba-González, D.Dominique Feillet, M.Maxime Ogier and F.Frédéric Semet. Integrating picker congestion in the solving of the Joint Order Batching and Picker Routing Problem.ROADEF 2024: 25ème congrès de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionAmiens, FranceMarch 2024HALback to text

Conferences without proceedings

• 32 inproceedingsH.Hadrien Cambazard, N.Nicolas Catusse and M.Maxime Ogier. Dynamic programming state space restrictions in column generation for order batching.ROADEF 2024: 25ème congrès de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionAmiens, FranceMarch 2024HAL
• 33 inproceedingsH.Hadrien Cambazard, N.Nicolas Catusse and M.Maxime Ogier. Dynamic programming state space restrictions to solve the joint order batching and picker routing problem via column generation.EURO 2024-33rd Conference of the Association of European Operational Research SocietiesCopenhague, DenmarkJuly 2024HALback to text
• 34 inproceedingsC.Clément Legrand, D.Diego Cattaruzza, L.Laetitia Jourdan and M.-E.Marie-Eléonone Kessaci. Neighborhood Exploration Strategies for a Sequence-aware MOEA/D to Solve a bVRPTW.ROADEF 2024Amiens, FranceMarch 2024HAL
• 35 inproceedingsA.Aymane Lotfi, M.Matteo Petris, T.Thibaut Cuvelier, B.Bruno De Backer and C.Claudia Archetti. A Novel Instance Generator for Simulating Middle-Mile Logistics Networks.33rd European Conference on Operational ResearchCopenhagen, DenmarkJuly 2024HAL
• 36 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
• 37 inproceedingsW.Wenjiao Sun, M.Maxime Ogier and F.Frédéric Semet. A column generation based heuristic for the integrated vehicle routing and driver scheduling problem.ROADEF 2024: 25ème congrès de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionAmiens, FranceMarch 2024HALback to text
• 38 inproceedingsP.Pablo Torrealba-González, D.Dominique Feillet, M.Maxime Ogier and F.Frédéric Semet. Estimating and Optimizing Congestion Delay in Picker-to-Part Systems.First Workshop on Warehouse Management and LogisticsLille, FranceOctober 2024HALback to text

Scientific book chapters

• 39 inbookB.Bernard Gendron, P.-V.Paul-Virak Khuong and F.Frédéric Semet. Models and Methods for Two-Level Uncapacitated Facility Location problems.358Contributions to Combinatorial Optimization and ApplicationsInternational Series in Operations Research & Management ScienceSpringer Nature Switzerland2024, 59-75HALDOIback to text

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

• 40 proceedingsDecision-focused predictions via pessimistic bilevel optimization: a computational study.Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2024)14742Lecture Notes in Computer ScienceSpringer Nature Switzerland2024, 127-135HALDOI
• 41 proceedingsNetwork Games, Artificial Intelligence, Control and Optimization.11th International Conference NETGCOOP 2024LNCS 15185February 2025, 159HAL

Reports & preprints

• 42 miscM. F.Miguel F Anjos, L.Luce Brotcorne and G.Gaël Guillot. Optimal Electric Vehicle Charging with Dynamic Pricing, Customer Preferences and Power Peak Reduction.November 2024HAL
• 43 miscV.Victor Bucarey, N.Natividad González-Blanco, M.Martine Labbé and J.Juan Mesa. On λ-Cent-Dians and Generalized-Center for Network Design.2024HALDOIback to text
• 44 miscM.Marta Fochesato, F.Frédy Pokou, H.Hélène Le Cadre and J.John Lygeros. Noncooperative Games with Prospect Theoretic Preferences.December 2024HALback to text
• 45 miscM.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.February 2024HAL
• 46 miscT.Thibault Prunet, N.Nabil Absi, V.Valeria Borodin and D.Diego Cattaruzza. Optimization of Human-Aware Manufacturing and Logistics Systems: A Comprehensive Review of Modeling Approaches and Applications.June 2024, 100136HAL
• 47 miscT.Thibault Prunet, N.Nabil Absi, V.Valeria Borodin and D.Diego Cattaruzza. Optimization of Human-Aware Manufacturing and Logistics Systems: A Survey on the Modeling Frameworks of Human Aspects.2024, 100137HAL
• 48 miscM.Mariam Sangaré, L.Luce Brotcorne, H.Hélène Le Cadre and M.Michael Poss. Cooperative game theory and cost allocation in energy communities.November 2024HAL
• 49 miscJ.Juan Sepúlveda, L.Luce Brotcorne and H.Hélène Le Cadre. A Reverse Stackelberg Model for Demand Response in Local Energy Markets.November 2024HALback to text

Other scientific publications

• 50 miscJ.Jed Boufaied, J.Jean Le Hénaff and H.Hélène Le Cadre. Strategic Stability Analysis of a Nonlinear Security Game.Amiens, FranceMarch 2024HALback to text
• 51 miscF.Fredy Pokou, H. L.Hélène Le Cadre and M.Marta Fochesato. Supplementary material for "Tariff Versus Sanction Under Bounded Rationality".June 2024HAL