An adaptive search heuristic for the capacitated fixed charge location problem

Authors:
Harry Venables;Alfredo Moscardini
Affiliations:
Sunderland Business School, University of Sunderland, United Kingdom;School of Computing & Technology, University of Sunderland, United Kingdom
Venue:
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Year:
2006

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Abstract

The Capacitated Fixed Charge Location Problem (CFCLP) consists of selecting a set of facilities that must completely supply a set of customers at a minimum cost. The CFCLP is NP-hard thus solution methods are often obtained by using sophisticated techniques. However, if a set of facilities is known a priori then the CFCLP reduces to a transportation problem (TP). Although this can be used to derive solutions by randomly selecting sufficient facilities to be fixed open and noting any cost improvements, it is perceived as a poor technique that does not guarantee solutions near the optimal. This paper presents an adaptive sampling algorithm using Ant Colony Optimization (ACO). We hypothesize that random selection of facilities using ACO will generate at least near-optimal solutions for the CFCLP. Computational results for a series of standard benchmark problems are presented which appear to confirm this hypothesis.