Several flow shop scheduling problems with truncated position-based learning effect

Xiao Yuan Wang, Zhili Zhou, Xi Zhang, Ping Ji, Ji Bo Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)


The concept of truncated position-based learning process plays a key role in production environments. However, it is relatively unexplored in the flow shop setting. In this paper, we consider the flow shop scheduling with truncated position-based learning effect, i.e., the actual processing time of a job is a function of its position and a control parameter in a processing permutation. The objective is to minimize one of the six regular performance criteria, namely, the total completion time, the makespan, the total weighted completion time, the discounted total weighted completion time, the sum of the quadratic job completion times, and the maximum lateness. We present heuristic algorithms and analyze the worst-case bound of these heuristic algorithms. We also provide the computational results to evaluate the performance of the heuristics.
Original languageEnglish
Pages (from-to)2906-2929
Number of pages24
JournalComputers and Operations Research
Issue number12
Publication statusPublished - 5 Aug 2013


  • Flow shop
  • Heuristic algorithm
  • Learning effect
  • Regular performance criteria
  • Scheduling
  • Worst-case analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research


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