Abdelkader Sbihi

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In this paper, we approximately solve the multiple-choice multi-dimensional knapsack problem. We propose an algorithm which is based upon reactive local search and where an explicit check for the repetition of configurations is added to the local search. The algorithm starts by an initial solution and improved by using a fast iterative procedure. Later,(More)
The basic Vehicle Routing and Scheduling Problem (VRSP) is described followed by an outline of solution approaches. Different variations of the basic VRSP are examined that involve the consideration of additional constraints or other changes in the structure of the appropriate model. An introduction is provided to Green Logistics issues that are relevant to(More)
0377-2217/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.ejor.2009.05.033 * Tel.: +33 240 374 645; fax: +33 821 908 824. E-mail address: asbihi@audencia.com The purpose of this article is to present a novel method to approximately solve the Multiple-Scenario Max–Min Knapsack Problem (MSMKP). This problem models many real world situations, e.g. when(More)
The Knapsack Sharing Problem (KSP) is an NP-Hard combinatorial optimization problem, admitted in numerous real world applications. In the KSP, we have a knapsack of capacity c and a set of n objects, namely N , where each object j, j = 1, . . . , n, is associated with a profit p j and a weight w j . The set of objects N is composed of m different classes of(More)
The aim of this paper is segmenting a sequence of images containing dynamic textures. The proposed method is based on means of features extracted from spatio-temporal cooccurrence matrices that characterize the textures themselves as well as their movements. Features with the highest discriminating power are selected according to a supervised scheme that(More)
The present work aims to bring out a new approach of features selection for the discrimination of textures in sequences of images containing moving objects. The moving textures are analysed using spatio-temporal co-occurrence matrices from which we extract features characterizing the textures themselves as well as their movements. The originality of this(More)