Single-machine scheduling with a time-dependent learning effect

J. B. Wang, Chi To Ng, Edwin Tai Chiu Cheng, L. L. Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

87 Citations (Scopus)

Abstract

In this paper we consider the single-machine scheduling problem with a time-dependent learning effect. The time-dependent learning effect of a job is assumed to be a function of the total normal processing time of the jobs scheduled in front of the job. We show by examples that the optimal schedule for the classical version of the problem is not optimal in the presence of a time-dependent learning effect for the following three objective functions: the weighted sum of completion times, the maximum lateness and the number of tardy jobs. But for some special cases, we prove that the weighted shortest processing time (WSPT) rule, the earliest due date (EDD) rule and Moore's Algorithm can construct an optimal schedule for the problem to minimize these objective functions, respectively. We use these three rules as heuristics for the general cases and analyze their worst-case error bounds. We also provide computational results to evaluate the performance of the heuristics.
Original languageEnglish
Pages (from-to)802-811
Number of pages10
JournalInternational Journal of Production Economics
Volume111
Issue number2
DOIs
Publication statusPublished - 1 Feb 2008

Keywords

  • Learning effect
  • Scheduling
  • Single machine
  • Time-dependent

ASJC Scopus subject areas

  • Economics and Econometrics
  • Industrial and Manufacturing Engineering

Cite this