Parallel algorithms for continuous multifacility competitive location problems

Parallel algorithms for continuous multifacility competitive location problems

0.00 Avg rating0 Votes
Article ID: iaor20117990
Volume: 50
Issue: 4
Start Page Number: 557
End Page Number: 573
Publication Date: Aug 2011
Journal: Journal of Global Optimization
Authors: , , ,
Keywords: allocation: resources, optimization, cost benefit analysis, heuristics, combinatorial optimization
Abstract:

We consider a continuous location problem in which a firm wants to set up two or more new facilities in a competitive environment. Both the locations and the qualities of the new facilities are to be found so as to maximize the profit obtained by the firm. This hard‐to‐solve global optimization problem has been addressed in Redondo et al. (2009) using several heuristic approaches. Through a comprehensive computational study, it was shown that the evolutionary algorithm uego is the heuristic which provides the best solutions. In this work, uego is parallelized in order to reduce the computational time of the sequential version, while preserving its capability at finding the optimal solutions. The parallelization follows a coarse‐grain model, where each processing element executes the uego algorithm independently of the others during most of the time. Nevertheless, some genetic information can migrate from a processor to another occasionally, according to a migratory policy. Two migration processes, named Ring‐Opt and Ring‐Fusion2, have been adapted to cope the multiple facilities location problem, and a superlinear speedup has been obtained.

Reviews

Required fields are marked *. Your email address will not be published.