A Particle Swarm Optimization Algorithm On Job-Shop Scheduling Problems With Multi-Purpose Machines
AbstractThis paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOc is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOc in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOc combined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems.
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Bibliographic InfoArticle provided by World Scientific Publishing Co. Pte. Ltd. in its journal Asia-Pacific Journal of Operational Research.
Volume (Year): 26 (2009)
Issue (Month): 02 ()
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Web page: http://www.worldscinet.com/apjor/apjor.shtml
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