Makespan minimization for m-machine permutation flowshop scheduling problem with learning considerations

Yu Hsiang Chung*, Lee-Ing Tong

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Studies on scheduling with learning considerations have recently become important. Most studies focus on single-machine settings. However, numerous complex industrial problems can be modeled as flowshop scheduling problems. This paper thus focuses on minimizing the makespan in an m-machine permutation flowshop with learning considerations. This paper proposes a dominance theorem and a lower bound to accelerate the branch-and-bound algorithm for seeking the optimal solution. This paper also adapts four well-known existing heuristic algorithms to yield the near-optimal solutions. Eventually, the performances of all the algorithms proposed in this paper are reported for small and large job-sized problems. The computational experiments indicate that the branch-and-bound algorithm can solve problems of up to 18 jobs within a reasonable amount of time, and the heuristic algorithms are quite accurate with a mean error percentage of less than 0.1%.

Original languageEnglish
Pages (from-to)355-367
Number of pages13
JournalInternational Journal of Advanced Manufacturing Technology
Volume56
Issue number1-4
DOIs
StatePublished - 1 Sep 2011

Keywords

  • Flowshop
  • Learning effects
  • Makespan
  • Scheduling

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