A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm

Jian Hung Chen, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

In this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance, greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously without using heuristics and a set of good non-dominated solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA for production planning of FMSs.

Original languageEnglish
Pages (from-to)949-957
Number of pages9
JournalInternational Journal of Machine Tools and Manufacture
Volume45
Issue number7-8
DOIs
StatePublished - 1 Jun 2005

Keywords

  • Flexible manufacturing system
  • Genetic algorithm
  • Multi-objective optimization
  • Production planning

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