A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems

  • Authors:

  • Hongbo Liu;Ajith Abraham;Zuwen Wang

  • Affiliations:

  • School of Information Science and Technology, Dalian Maritime University Dalian 116026, China. E-mail: lhb@dlut.edu.cn;Machine Intelligence Research Labs - MIR Labs, http://www.mirlabs.org Auburn, Washington 98071 USA. E-mail: ajith.abraham@ieee.org;School of Electromechanics and Materials Engineering, Dalian Maritime University Dalian 116026, China. E-mail: wangzw@dlmu.edu.cn

  • Venue:
  • Fundamenta Informaticae - Swarm Intelligence
  • Year:
  • 2009

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Abstract

Swarm Intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. In this paper, we model the scheduling problem for the multi-objective Flexible Job-shop Scheduling Problems (FJSP) and attempt to formulate and solve the problem using a Multi Particle Swarm Optimization (MPSO) approach. MPSO consists of multi-swarms of particles, which searches for the operation order update and machine selection. All the swarms search the optima synergistically and maintain the balance between diversity of particles and search space. We theoretically prove that the multi-swarm synergetic optimization algorithm converges with a probability of 1 towards the global optima. The details of the implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP, especially for large scale problems.