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- W2080752052 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-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." @default.
- W2080752052 created "2016-06-24" @default.
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- W2080752052 date "2009-04-01" @default.
- W2080752052 modified "2023-09-25" @default.
- W2080752052 title "A PARTICLE SWARM OPTIMIZATION ALGORITHM ON JOB-SHOP SCHEDULING PROBLEMS WITH MULTI-PURPOSE MACHINES" @default.
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- W2080752052 doi "https://doi.org/10.1142/s0217595909002158" @default.
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