Scheduling with a position-weighted learning effect

Edwin Tai Chiu Cheng, Wen Hung Kuo, Dar Li Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


In general, human learning takes time to build up, which results from a worker gaining experience from repeating similar operations over time. In the early stage of processing a given set of similar jobs, a worker is not familiar with the operations, so his learning effect on the jobs scheduled early is not apparent. On the other hand, when the worker has gained experience in processing the jobs his learning improves. So a worker's learning effect on a job depends not only on the total processing time of the jobs that he has processed but also on the job position. In this paper we introduce a position-weighted learning effect model for scheduling problems. We provide optimal solutions for the single-machine problems to minimize the makespan and the total completion time, and an optimal solution for the single-machine problem to minimize the total tardiness under an agreeable situation. We also consider two special cases of the flowshop problem.
Original languageEnglish
Pages (from-to)293-306
Number of pages14
JournalOptimization Letters
Issue number1
Publication statusPublished - 1 Jan 2014


  • Learning effect
  • Makespan
  • Scheduling
  • Total completion time
  • Total tardiness

ASJC Scopus subject areas

  • Control and Optimization


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