This thesis aims to present families of fully and partially discretised formulations for vehicle routing problems with delivery decisions. These formulations bridge an existing gap in the literature for the targeted problems, namely SDVRP variants, with or without time windows, and the IRP. Existing BCP implementations in the literature for these problems rely on a concept that may require several rounds of column generation. This detrimental effect is not present in our proposed formulations. In addition, we adapt existing and novel families of non-robust inequalities for these problems by employing the limited-memory technique. Several cut separation procedures are implemented to separate robust and non-robust inequalities. Especially for SDVRP variants, we highlight that our separation algorithms are generic and capable of finding valid cuts violated for both CVRP and SDVRP variants.
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Authors: Patxi Flambard, Ayse Nur Arslan, Boris Detienne
Abstract: In this paper, we consider two-stage linear adjustable robust optimization problems with continuous and fixed recourse. These problems have been the subject of exact solution approaches, notably constraint generation and constraint-and-column generat [Read more]
Abstract: In this paper, we consider two-stage linear adjustable robust optimization problems with continuous and fixed recourse. These problems have been the subject of exact solution approaches, notably constraint generation and constraint-and-column generat [Read more]
Authors: Fulin Yan, François Clautiaux, Aurélien Froger, Boris Albar
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Abstract: In this work, we propose a generic heuristic for the resource-constrained shortest path problem derived from dynamic programming reformulations of hard combinatorial optimization problems. The approach is a machine-learning (ML)-augmented beam search [Read more]
Authors: Boris Detienne, Mickaël Gaury, Gautier Stauffer
Abstract: We introduce the Kit Update Problem, a new online optimization problem motivated by a real-world application at Doctors Without Borders. Emergency response kits-pre-packed sets of medical items-must be kept in a ready-to-ship state at all times. Sinc [Read more]
Abstract: We introduce the Kit Update Problem, a new online optimization problem motivated by a real-world application at Doctors Without Borders. Emergency response kits-pre-packed sets of medical items-must be kept in a ready-to-ship state at all times. Sinc [Read more]
We are happy to announce that Lionel Rolland has joined our team as a CIFRE PhD student with EDF. Lionel will work on the optimization of a hydro valley.
We are pleased to announce that a permanent associate professor position (maître de conférences) at the University of Bordeaux is available for the 2025 national recruitment campaign. The successful candidate will join the Edge team.
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We are happy to announce that Paul Archipczuk has joined our team as an engineer. Paul will work on the project Grip4all about robotic palletizing of heterogeneous products without prior scheduling.
We are happy to announce that Pierre Pinet has joined our team as a CIFRE PhD student with Saint-Gobain. Pierre will work on solving routing problems under uncertainty.
Authors: François Clautiaux , Ivana Ljubić
Abstract: Mixed-integer linear programming (MILP) has become a cornerstone of operations research. This is driven by the enhanced efficiency of modern solvers, which can today find globally optimal solutions within seconds for problems that were out of reach a [Read more]
Abstract: Mixed-integer linear programming (MILP) has become a cornerstone of operations research. This is driven by the enhanced efficiency of modern solvers, which can today find globally optimal solutions within seconds for problems that were out of reach a [Read more]
Authors: Sylvain Lichau, Ruslan Sadykov, Julien François, Rémy Dupas
Abstract: In this paper, we propose a new set-partitioning model for the two-echelon vehicle routing problem with drones (2E-VRP-D), where partial routes corresponding to drone movements are enumerated using an efficient dynamic program. To solve the model we [Read more]
Abstract: In this paper, we propose a new set-partitioning model for the two-echelon vehicle routing problem with drones (2E-VRP-D), where partial routes corresponding to drone movements are enumerated using an efficient dynamic program. To solve the model we [Read more]
We are happy to announce that Mariam Sangare has joined our team as a postdoctoral researcher. Mariam will work on the EDF-Inria Challenge on generation expansion planning problems. Prior to her arrival in the team Mariam has completed her PhD thesis at University of Montpellier under the supervision of Michael Poss.
Authors: Maryam Daryalal, Ayse N. Arslan, Merve Bodur
Abstract: In this work, we design primal and dual bounding methods for multistage adaptive robust optimization (MSARO) problems motivated by two decision rules rooted in the stochastic programming literature. From the primal perspective, this is achieved by ap [Read more]
Abstract: In this work, we design primal and dual bounding methods for multistage adaptive robust optimization (MSARO) problems motivated by two decision rules rooted in the stochastic programming literature. From the primal perspective, this is achieved by ap [Read more]
Authors: Ayse N. Arslan, Michael Poss
Abstract: Uncertainty reduction has recently been introduced in the robust optimization literature as a relevant special case of decision-dependent uncertainty. Herein, we identify two relevant situations in which the problem is polynomially solvable. We provi [Read more]
Abstract: Uncertainty reduction has recently been introduced in the robust optimization literature as a relevant special case of decision-dependent uncertainty. Herein, we identify two relevant situations in which the problem is polynomially solvable. We provi [Read more]
Authors: Luis Marques, François Clautiaux, Aurélien Froger
Abstract: We study the problem of designing a cabinet made up of a set of shelves that contain compartments whose contents slide forward on opening. Considering a set of items candidate to be stored in the cabinet over a given time horizon, the problem is to d [Read more]
Abstract: We study the problem of designing a cabinet made up of a set of shelves that contain compartments whose contents slide forward on opening. Considering a set of items candidate to be stored in the cabinet over a given time horizon, the problem is to d [Read more]
Authors: Xuan Ren, Aurélien Froger, Ola Jabali, Gongqian Liang
Abstract: We propose a heuristic algorithm capable of handling multiple variants of the vehicle routing problem with drones (VRPD). Assuming that the drone may be launched from a node and recovered at another, these variants are characterized by three axes, (1 [Read more]
Abstract: We propose a heuristic algorithm capable of handling multiple variants of the vehicle routing problem with drones (VRPD). Assuming that the drone may be launched from a node and recovered at another, these variants are characterized by three axes, (1 [Read more]
Authors: Brais González-Rodrı́guez, Aurélien Froger, Ola Jabali, Joe Naoum-Sawaya
Abstract: Establishing the size of an EV fleet is a vital decision for logistics operators. In urban settings, this issue is often dealt with by partitioning the geographical area around a depot into service zones, each served by a single vehicle. Such zones u [Read more]
Abstract: Establishing the size of an EV fleet is a vital decision for logistics operators. In urban settings, this issue is often dealt with by partitioning the geographical area around a depot into service zones, each served by a single vehicle. Such zones u [Read more]
Authors: Ayse N. Arslan, Jeremy Omer, Fulin Yan
Abstract: The kidney exchange problem (KEP) is an increasingly important healthcare management problem in most European and North American countries which consists of matching incompatible patient-donor pairs in a centralized system. Despite the significant pr [Read more]
Abstract: The kidney exchange problem (KEP) is an increasingly important healthcare management problem in most European and North American countries which consists of matching incompatible patient-donor pairs in a centralized system. Despite the significant pr [Read more]
Authors: Boris Detienne, Henri Lefebvre, Enrico Malaguti, Michele Monaci
Abstract: In this work, we study optimization problems where some cost parameters are not known at decision time and the decision flow is modeled as a two-stage process within a robust optimization setting. We address general problems in which all constraints [Read more]
Abstract: In this work, we study optimization problems where some cost parameters are not known at decision time and the decision flow is modeled as a two-stage process within a robust optimization setting. We address general problems in which all constraints [Read more]
We are happy to announce that Najib Errami has joined our team as an engineer. Najib will work on the project ADLib which aims to create a library for Aggregation/Disaggregation of large network flow models.
This thesis focuses on algorithmic design for three combinatorial optimization problems related to transportation, logistics and production research with specific types of indus- trial constraints. First, we consider the Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP). This problem is an extension of two well-known combinatorial optimization problems — the Generalized Traveling Salesman Problem (GTSP) and the Precedence Constrained Asymmetric Traveling Salesman Problem (PCATSP), whose path version is known as the Sequential Ordering Problem (SOP).
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Authors: François Clautiaux, Siham Essodaigui, Alain Nguyen, Ruslan Sadykov, Nawel Younes
Abstract: In this paper, we consider a new industrial problem, which occurs in the context of the automobile industry. This problem occurs during the testing phase of a new vehicle. It involves determining all the variants of the vehicle to be manufactured in [Read more]
Abstract: In this paper, we consider a new industrial problem, which occurs in the context of the automobile industry. This problem occurs during the testing phase of a new vehicle. It involves determining all the variants of the vehicle to be manufactured in [Read more]
Authors: Najib Errami, Eduardo Queiroga, Ruslan Sadykov, Eduardo Uchoa
Abstract: The optimization community has made significant progress in solving Vehicle Routing Problems (VRPs) to optimality using sophisticated Branch-Cut-and-Price (BCP) algorithms. VRPSolver is a BCP algorithm with excellent performance in many VRP variants. [Read more]
Abstract: The optimization community has made significant progress in solving Vehicle Routing Problems (VRPs) to optimality using sophisticated Branch-Cut-and-Price (BCP) algorithms. VRPSolver is a BCP algorithm with excellent performance in many VRP variants. [Read more]
Authors: Xavier Blanchot, François Clautiaux, Aurélien Froger, Manuel Ruiz
Abstract: In this paper, we study how a regulatory constraint limiting a measure of unserved demand, called Loss Of Load Expectation (LOLE), can be incorporated into a strategic version of a stochastic generation and transmission expansion planning problem. Th [Read more]
Abstract: In this paper, we study how a regulatory constraint limiting a measure of unserved demand, called Loss Of Load Expectation (LOLE), can be incorporated into a strategic version of a stochastic generation and transmission expansion planning problem. Th [Read more]
Authors: Maryam Daryalal, Ayse N. Arslan, Merve Bodur
Abstract: In this work, we design primal and dual bounding methods for multistage adjustable robust optimization (MSARO) problems motivated by two decision rules rooted in the stochastic programming literature. From the primal perspective, this is achieved by [Read more]
Abstract: In this work, we design primal and dual bounding methods for multistage adjustable robust optimization (MSARO) problems motivated by two decision rules rooted in the stochastic programming literature. From the primal perspective, this is achieved by [Read more]