Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry

Z. X. Guo, Wai Keung Wong, S. Y.S. Leung, J. T. Fan, S. F. Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

70 Citations (Scopus)

Abstract

An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each order's production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment.
Original languageEnglish
Pages (from-to)202-219
Number of pages18
JournalComputers and Industrial Engineering
Volume50
Issue number3
DOIs
Publication statusPublished - 1 Jul 2006

Keywords

  • Apparel industry
  • Genetic algorithm
  • Job shop scheduling
  • Mathematical model
  • Optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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