A particle swarm optimization algorithm on job-shop scheduling problems with multi-purpose machines

A particle swarm optimization algorithm on job-shop scheduling problems with multi-purpose machines

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Article ID: iaor200969277
Country: Singapore
Volume: 26
Issue: 2
Start Page Number: 161
End Page Number: 184
Publication Date: Mar 2009
Journal: Asia-Pacific Journal of Operational Research
Authors: ,
Keywords: heuristics
Abstract:

This 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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