Title: A metaheuristic for solving the unsplittable multicommodity flow problem: the maritime surveillance case

Authors: Hela Masri; Saoussen Krichen; Adel Guitouni

Addresses: Larodec Laboratory, Institut Supérieur de Gestion de Tunis, 2000 Bardo, Tunisia ' Larodec Laboratory, Institut Supérieur de Gestion de Tunis, 2000 Bardo, Tunisia ' Peter B. Gustavson School of Business, University of Victoria, Victoria (British-Columbia), Canada

Abstract: In this paper, we address the problem of routing optimisation in the backbone of a surveillance network. A surveillance mission is characterised by the employment and collaboration of several agents processing diverse information sources' inputs in order to ensure a surveillance task. These communications rely in a predefined network infrastructure containing mobile nodes and static backbone nodes. As the backbone network is private and configurable, a global routing algorithm can be set up in order to optimise information exchange. We propose to model this problem as a single path multicommodity flow problem (SMCFP), where several commodities are to be shared. The considered objective function is to minimise the overall network congestion. As the complexity of the SPMFP is NP-Hard, a multi-start tabu search (MTS) is proposed as a solution approach. The empirical validation is done using a simulation environment called Inform Lab containing real instances of surveillance cases. A comparison to a state-of-the-art ant colony system (ACS) approach is performed based on two large testbeds. The same instances are optimally solved using CPLEX. The experimental results show that the MTS produces considerably better results than the ACS.

Keywords: surveillance networks; routing optimisation; multicommodity flow problem; MCFP; metaheuristics; maritime surveillance; mobile nodes; static backbone nodes; information exchange; network congestion; multi-start tabu search; simulation; ant colony optimisation; ACO.

DOI: 10.1504/IJBIDM.2014.068368

International Journal of Business Intelligence and Data Mining, 2014 Vol.9 No.3, pp.254 - 269

Published online: 10 Apr 2015 *

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