An efficient job-shop scheduling algorithm based on particle swarm optimization

  • Authors:

  • Tsung-Lieh Lin;Shi-Jinn Horng;Tzong-Wann Kao;Yuan-Hsin Chen;Ray-Shine Run;Rong-Jian Chen;Jui-Lin Lai;I-Hong Kuo

  • Affiliations:

  • Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan and Department of Computer Science and Information Engineering, National Taiwan Unive ...;Department of Electronic Engineering, Technology and Science Institute of Northern Taiwan, Taipei, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

The job-shop scheduling problem has attracted many researchers' attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of particle swarm optimization, simulated annealing technique and multi-type individual enhancement scheme is presented to solve the job-shop scheduling problem. The experimental results show that the new proposed job-shop scheduling algorithm is more robust and efficient than the existing algorithms.