Thibaut Lust

Sorbonne Université | UPMC · Laboratoire d'informatique de Paris 6 (LIP6)

Various practical optimization problems can be formalized as the search of an optimal independent set in a matroid. When the set function to be optimized is additive, this problem can be exactly solved using a greedy algorithm. However, in some situations, the set function is not exactly known and must be elicited before or during the optimization...

In this paper, we develop a general interactive method to solve multi-objective combinatorial optimization problems with imprecise preferences. Assuming that preferences can be represented by a parameterized scalarizing function, we iteratively ask preferences queries to the decision maker in order to reduce the uncertainty over the preference para...

We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. More precisely, for linear objective (utility) functions, we propose an interactive greedy algorithm interleaving preference queries with the incremental construction of an independent set to obtain an optimal or n...

We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinatorial optimization problems with unknown preferences. For the purpose of elicitation, we assume that the decision maker's preferences can be represented by a parameterized scalarizing function...

We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinato-rial optimization problems with unknown preferences. For the purpose of elicitation, we assume that the decision maker's preferences can be represented by a pa-rameterized scalarizing functio...

In this paper, we develop a general interactive polyhedral approach to solve multi-objective combinatorial optimization problems with incomplete preference information. Assuming that preferences can be represented by a parameterized scalarizing function, we iteratively ask preferences queries to the decision maker in order to reduce the imprecision...

In this paper, we propose a general approach based on local search and incremental preference elicitation for solving multi-objective combinatorial optimization problems with imprecise preferences. We assume that the decision maker’s preferences over solutions can be represented by a parameterized scalarizing function but the parameters are initial...

This article deals with the bi‐objective pollution‐routing problem (bPRP), a vehicle routing variant that arises in the context of green logistics. The two conflicting objectives considered are the minimization of the CO2 emissions and the costs related to driver's wages. A multi‐objective approach based on the two‐phase Pareto local search heurist...

In this paper we propose a new method called ND-Tree-based update (or shortly ND-Tree) for the dynamic non-dominance problem, i.e. the problem of online update of a Pareto archive composed of mutually non-dominated points. It uses a new ND-Tree data structure in which each node represents a subset of points contained in a hyperrectangle defined by...

In this paper we propose simple yet efficient version of the two-phase Pareto local search (2PPLS) for solving the biobjective traveling salesman problem (bTSP). In the first phase the powerful Lin–Kernighan heuristic is used to generate some high quality solutions being very close to the Pareto front. Then Pareto local search is used to generate m...

In this paper we propose a new method called ND-Tree for fast online update of a Pareto archive composed of mutually non-dominated solutions. ND-Tree uses a tree structure in which each node represents a subset of solutions contained in a hypercube defined by its local approximate ideal and nadir points. A leaf is a subset of solutions organized as...

This paper deals with biobjective combinatorial optimization problems where both objectives are required to be well-balanced. Lorenz dominance is a refinement of the Pareto dominance that has been proposed in economics to measure the inequalities in income distributions. We consider in this work the problem of computing the Lorenz optimal solutions...

In this paper, we address the problem of comparing the performances of two popular aggregation operators, the weighted sum and the Choquet integral, for selecting the best alternative among a set of alternatives, all evaluated according to different criteria. While the weighted sum is simple to use and very popular, the Choquet integral is still ha...

Having observed low success rates among first-year university students in both Belgium and France, we develop prediction models in this paper in order to identify, at the earliest possible stage, those students who are at risk of failing at the end of the academic year. We applied different data mining techniques to predict the students' academic s...

In this paper, we propose a sufficient condition for a solution to be optimal for a 2-additive Choquet integral in the context of multiobjective combinatorial optimization problems. A 2-additive Choquet optimal solution is a solution that optimizes at least one set of parameters of the 2-additive Choquet integral. We also present a method to genera...

In this paper, we study the multiobjective version of the set covering problem. To our knowledge, this problem has only been addressed in two papers before, and with two objectives and heuristic methods. We propose a new heuristic, based on the two-phase Pareto local search, with the aim of generating a good approximation of the Pareto efficient so...

We study in this paper the computation of Choquet optimal solutions in decision contexts involving multiple criteria or multiple agents. Choquet optimal solutions are solutions that optimize a Choquet integral, one of the most powerful tools in multicriteria decision making. We develop a new property that characterizes the Choquet optimal solutions...

In this paper, we study the biobjective version of the set covering problem. To our knowledge, this problem has only been addressed in two papers before, and with heuristic methods. We propose a new heuristic, based on the two-phase Pareto local search, with the aim of generating a good approximation of the Pareto efficient solutions. In the first...

We study in this paper the generation of the Choquet optimal solutions of biobjective combinatorial optimization problems. Choquet optimal solutions are solutions that optimize a Choquet integral. The Choquet integral is used as an aggregation function, presenting different parameters, and allowing to take into account the interactions between the...

This work presents three multi-objective heuristic algorithms based on Two-phase Pareto Local Search with VNS (2PPLS-VNS), Multi-objective Variable Neighborhood Search (MOVNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The algorithms were applied to the open-pit-mining operational planning problem with dynamic truck allocation (OPMOP...

L’ordonnancement journalier du bloc opératoire est un problème complexe fortement contraint et présentant différents objectifs `à optimiser. Dans cet article, nous cherchons, non seulement `à minimiser le makespan, à minimiser le coût des heures supplémentaires, `à maximiser les affinités dans la composition d’une ´équipe chirurgicale, mais aussi à...

Very large-scale neighborhood search (VLSNS) is a technique intensively used in single-objective optimization. However, there is almost no study of VLSNS for multiobjective optimization. We show in this paper that this technique is very efficient for the resolution of multiobjective combinatorial optimization problems. Two problems are considered:...

Mots-clés : optimisation combinatoire biobjectif, optimisation équitable, optimisation ro-buste, dominance de Lorenz, méthodes en deux phases. Dans ce travail, nous nous intéressons à la détermination des solutions optimales selon la dominance de Lorenz (appelées solutions Lorenz-optimales) pour des problèmes d'optimisation combinatoire multiobject...

The knapsack problem (KP) and its multidimensional version (MKP) are basic problems in combinatorial optimization. In this paper we consider their multiobjective extension (MOKP and MOMKP), for which the aim is to obtain or to approximate the set of efficient solutions. In a first step, we classify and describe briefly the existing works, that are...

We consider the following problem: to decompose a nonnegative integer matrix into a linear combination of binary matrices that respect the consecutive ones prop- erty. This problem occurs in the radiotherapy treatment of cancer. The nonnegative integer matrix corresponds to fields giving the different radiation beams that a linear accelerator has t...

In this work, we present a method, called Two-Phase Pareto Local Search, to find a good approximation of the efficient set of the biobjective traveling salesman problem. In the first phase of the method, an initial population composed of a good approximation of the extreme supported efficient solutions is generated. We use as second phase a Pareto...

In this paper, we present the Two-Phase Pareto Local Search (2PPLS) method with speed-up techniques for the heuristic resolution of the biobjective traveling salesman problem. The 2PPLS method is a state-of-the-art method for this problem. However, because of its running time that strongly grows with the instances size, the method can be hardly app...

We present in this paper, new resolution methods for the selective maintenance problem. This problem consists in finding the best choice of maintenance actions to be performed on a multicomponent system, so as to maximize the system reliability, within a time window of a limited duration. When the number of components of the system is important, th...

We consider the following problem: to decompose a positive integer matrix into a linear combination of binary matrices that respect the consecutive ones property. The positive integer matrix corresponds to fields giving the different radiation beams that a linear accelerator has to send throughout the body of a patient. Due to the inhomogeneous dos...

We first present a method, called Two-Phase Pareto Local Search, to find a good approximation of the efficient set of the biobjective traveling salesman problem. In the first phase of the method, an initial population composed of an approximation of the extreme supported efficient solutions is generated. We use as second phase a Pareto Local Search...

We present in this paper a new multiobjective memetic algorithm scheme called MEMOX. In current multiobjective memetic algorithms, the parents used for recombination are randomly selected. We improve this approach by using a dynamic hypergrid which allows to select a parent located in a region of minimal density. The second parent selected is a sol...

We first present in this paper the multicriteria case of the selective maintenance problem. This problem consists in finding the best choice of main-tenance actions to realize on a multicomponent system, so as to maximize the system reliability and minimize the maintenance cost, but with a limitation in the maintenance time. Since exact methods are...

A memetic algorithm (or genetic local search) is a genetic algorithm where the mutation op-erator is replaced by a local search method applied to every new o spring generated [9]. The memetic algorithms are particularly well adapted to the resolution of multiobjective optimiza-tion problems since a set of diversified solutions (from which the inter...

The traveling salesman problem (TSP) is a challenging problem in combinatorial optimization. In this paper we consider the multiobjective TSP for which the aim is to obtain or to approximate the set of efficient solutions. In a first step, we classify and describe briefly the existing works, that are essentially based on the use of metaheuristics....