PISUT PONGCHAIRERKS
Corresponding author.
Industrial Engineering Program, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12121, Thailand
VORATAS KACHITVICHYANUKUL
Industrial Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, 12120, Thailand

Received 15 May 2007
Accepted 26 April 2008

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-PSO_c, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSO_c is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSO_c in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSO_c 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.

Particle swarm optimization; job-shop scheduling; MPMJSP; makespan