Application of genetic approach for advanced planning in multi-factory environment

Sai Ho Chung, H. C.W. Lau, King Lun Tommy Choy, G. T.S. Ho, Y. K. Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)

Abstract

This paper deals with multi-factory production scheduling problems which consist of a number of factories. Each factory consists of various machines and is capable of performing various operations. Some factories may produce intermediate products and supply to other factories for assembly purpose, while some factories may produce finished products and supply to end customers. The model is subject to capacity constraints, precedence relationship, and alternative machining with different processing time. The problem encountered is to determine how to cope with each factory and machine in the system, and the objective is to minimize the makespan of a set of given jobs through proper collaboration. The makespan takes into account the processing time, transportation time between resources, and machine set-up time. This paper proposes a modified genetic algorithm to deal with the problem. The optimization reliability of the proposed algorithm has been tested by comparing it with existing approaches and simple genetic algorithms in several numerical examples found in literatures. The influence of different crossover and mutation rates on the performance of genetic search in simple genetic algorithms has also been demonstrated. The results also show the robustness of the proposed algorithm in this problem.
Original languageEnglish
Pages (from-to)300-308
Number of pages9
JournalInternational Journal of Production Economics
Volume127
Issue number2
DOIs
Publication statusPublished - 1 Oct 2010

Keywords

  • Assembly process
  • Genetic algorithms
  • Genetic parameters
  • Multi-factory production
  • Production scheduling

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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