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This paper is devoted to pricing optimization problems which can be modeled as bilevel programs. We present the main concepts, models and solution methods for this class of optimization problems.
A travelling deliveryman needs to find a tour such that the total waiting time of all the customers he has to visit is minimum. The deliveryman starts his tour at a depot, travelling at constant velocity. In this paper we suggest a general variable neighborhood search based heuristic to solve this NP-hard combinatorial optimization problem. We combine several classical neighborhood structures and...
In this paper, we propose a two-dimensional shelf space allocation model. The second dimension stems from the height of the shelf. This results in an integer nonlinear programming model with a complex form of objective function. We propose a multiple neighborhood approach which is a hybridization of a simulated annealing algorithm with a hyper-heuristic learning mechanism. Experiments based on empirical...
We introduce a new model for robust combinatorial optimization where the uncertain parameters belong to the image of multifunctions of the problem variables. In particular, we study the variable budgeted uncertainty, an extension of the budgeted uncertainty introduced by Bertsimas and Sim. Variable budgeted uncertainty can provide the same probabilistic guarantee as the budgeted uncertainty while...
The comparison of independent random variables can be modeled by a set of dice and a reciprocal relation expressing the winning probability of one dice over another. It is well known that dice transitivity is a necessary 3-cycle condition for a reciprocal relation to be dice representable, i.e. to be the winning probability relation of a set of dice. Although this 3-cycle condition is sufficient for...
During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided...
This is a summary of my PhD thesis supervised by Walid Ben-Ameur and defended on September 29 2009, at Télécom SudParis. The thesis is written in French language and is available from the author upon request at makhlouf.hadji@it-sudparis.eu.
We propose an extension of the FlowSort sorting method to the case when there is imprecision on the input data. Within multicriteria decision aid, a lot of attention has been paid to sorting problems where a set of actions has to be assigned to completely ordered categories. However, few methods suit when the data or the parameters of the model are not precisely defined. In this paper, instead of...
We establish strong NP-hardness and in-approximability of the so-called representatives selection problem, a tool selection problem in the area of robust optimization. Our results answer a recent question of Dolgui and Kovalev (4OR Q J Oper Res 10:181–192, 2012).
In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines...
In the field of multicriteria decision aid, considerable attention has been paid to supervised classification problems where the purpose is to assign alternatives into predefined ordered classes. In these approaches, often referred to as sorting methods, it is usually assumed that classes are either known a priori or can be identified by the decision maker. On the other hand, when the objective is...
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