Two-machine flowshop scheduling with truncated learning to minimize the total completion time

Der Chiang Li, Peng Hsiang Hsu, Chin Chia Wu, Edwin Tai Chiu Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

Scheduling with learning effects has received a lot of research attention lately. However, the flowshop setting is relatively unexplored. On the other hand, the actual processing time of a job under an uncontrolled learning effect will drop to zero precipitously as the number of jobs increases. This is rather absurd in reality. Motivated by these observations, we consider a two-machine flowshop scheduling problem in which the actual processing time of a job in a schedule is a function of the job's position in the schedule and a control parameter of the learning function. The objective is to minimize the total completion time. We develop a branch-and-bound and three simulated annealing algorithms to solve the problem. Computational results show that the proposed algorithms are efficient in producing near-optimal solutions.
Original languageEnglish
Pages (from-to)655-662
Number of pages8
JournalComputers and Industrial Engineering
Volume61
Issue number3
DOIs
Publication statusPublished - 1 Oct 2011

Keywords

  • Scheduling
  • Simulated annealing
  • Truncated learning function
  • Two-machine flowshop

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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