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Kanawati R.,CNRS Informatics Laboratory of Paris Nord
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

In this paper we propose a new approach for efficiently identifying local communities in complex networks. Most existing approaches are based on applying a greedy optimization process guided by a given objective function. Different objective functions have been proposed in the scientific literature, each capturing some specific feature of desired communities. In this work, we propose exploring a new multi-objective approach that allows combining different objective functions. First results obtained from experiments on benchmark networks argue for the relevancy of our approach. © 2014 IEEE. Source

Belenguer J.-M.,University Of Valncia | Benavent E.,University Of Valncia | Prins C.,University of Technology of Troyes | Prodhon C.,University of Technology of Troyes | Wolfler Calvo R.,CNRS Informatics Laboratory of Paris Nord
Computers and Operations Research

Recent researches in the design of logistic networks have shown that the overall distribution cost may be excessive if routing decisions are ignored when locating depots. The Location-Routing Problem (LRP) overcomes this drawback by simultaneously tackling location and routing decisions. The aim of this paper is to propose an exact approach based on a Branch-and-Cut algorithm for solving the LRP with capacity constraints on depots and vehicles. The proposed method is based on a zero-one linear model strengthened by new families of valid inequalities. The computational evaluation on three sets of instances (34 instances in total), with 510 potential depots and 2088 customers, shows that 26 instances with five depots are solved to optimality, including all instances with up to 40 customers and three with 50 customers. © 2010 Elsevier Ltd. All rights reserved. Source

Ngueveu S.U.,University of Technology of Troyes | Prins C.,University of Technology of Troyes | Wolfler Calvo R.,CNRS Informatics Laboratory of Paris Nord
Computers and Operations Research

The cumulative capacitated vehicle routing problem (CCVRP) is a transportation problem which occurs when the objective is to minimize the sum of arrival times at customers, instead of the classical route length, subject to vehicle capacity constraints. This type of challenges arises whenever priority is given to the satisfaction of the customer need, e.g. vital goods supply or rescue after a natural disaster. The CCVRP generalizes the NP-hard traveling repairman problem (TRP), by adding capacity constraints and a homogeneous vehicle fleet. This paper presents the first upper and lower bounding procedures for this new problem. The lower bounds are derived from CCVRP properties. Upper bounds are given by a memetic algorithm using non-trivial evaluations of cost variations in the local search. Good results are obtained not only on the CCVRP, but also on the special case of the TRP, outperforming the only TRP metaheuristic published. © 2009 Elsevier Ltd. All rights reserved. Source

Kanawati R.,CNRS Informatics Laboratory of Paris Nord
Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011

In this work we propose a new efficient algorithm for communities construction based on the idea that a community is animated by a set of leaders that are followed by a set of nodes. A node can follow different leaders animating different communities. The algorithm is structured into two mains steps: identifying nodes in the network playing the role of leaders, then assigning other nodes to some leaders. We provide a general framework for implementing such an approach. First experimental results obtained by applying the algorithm on different real networks show the effectiveness of the proposed approach. © 2011 IEEE. Source

Sedjelmaci S.M.,CNRS Informatics Laboratory of Paris Nord
ACM Communications in Computer Algebra

We present a new parallel algorithm which computes the GCD of n integers of O(n) bits in O(n/ log n) time with O(n2+ε) processors, for any ε > 0 on CRCW PRAM model. Source

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