A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment

King Lun Tommy Choy, Y. K. Leung, H. K.H. Chow, T. C. Poon, Chun Kit Kwong, G. T.S. Ho, S. K. Kwok

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)

Abstract

In the make-to-order (MTO) mode of manufacturing, the specification of each product is unique such that production processes vary from one product to another making the production schedule complex. In order to achieve high level productivity, the production flow is not arranged in sequence; instead, the job schedule of different production jobs is adjusted to fit in with the multiple-job shop environment. A poor scheduling of jobs leads to high production cost, long production time and tardiness in job performance. The existing of tardiness in the production schedule significantly affects the harmony among the multiple jobs on the shops floor. In order to provide a complete solution for solving MTO scheduling problems with job shifting and minimizing job tardiness, a hybrid scheduling decision support model (SDSM) is introduced. The model is combined by a Genetic Algorithm (GA) and an optimisation module. GA is adopted to solve the complex scheduling problem taking into consideration of the wide variety of processes while the optimisation module is suggested for tackling tardiness in doing the jobs in a cost effective way. The simulation results reveal that the model shortens the generation time of production schedules and reduces the production cost in MTO-based production projects.
Original languageEnglish
Pages (from-to)1931-1941
Number of pages11
JournalExpert Systems with Applications
Volume38
Issue number3
DOIs
Publication statusPublished - 1 Mar 2011

Keywords

  • Genetic Algorithm
  • Make-to-order
  • Operation management
  • Production planning
  • Scheduling

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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