A hybrid particle swarm optimization for job shop scheduling problem

  • Authors:

  • D. Y. Sha;Cheng-Yu Hsu

  • Affiliations:

  • Department of Business Administration, Asia University, 500 Liufeng Road, Wufong, Taichung 413, Taiwan, ROC and Department of Industrial Engineering and Management, National Chiao Tung University, ...;Department of Industrial Engineering and Management, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan, ROC

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2006

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Abstract

A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed in this paper. In previous research, PSO particles search solutions in a continuous solution space. Since the solution space of the JSP is discrete, we modified the particle position representation, particle movement, and particle velocity to better suit PSO for the JSP. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list concept in our algorithm. Giffler and Thompson's heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.