Single machine scheduling with truncated job-dependent learning effect

Xue Ru Wang, Ji Bo Wang, Jian Jin, Ping Ji

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)

Abstract

In this paper we consider the single machine scheduling problem with truncated job-dependent learning effect. By the truncated job-dependent learning effect, we mean that the actual job processing time is a function which depends not only on the job-dependent learning effect (i.e., the learning in the production process of some jobs to be faster than that of others) but also on a control parameter. The objectives are to minimize the makespan, the total completion time, the total absolute deviation of completion time, the earliness, tardiness and common (slack) due-date penalty, respectively. Several polynomial time algorithms are proposed to optimally solve the problems with the above objective functions.
Original languageEnglish
Pages (from-to)669-677
Number of pages9
JournalOptimization Letters
Volume8
Issue number2
DOIs
Publication statusPublished - 1 Feb 2014

Keywords

  • Learning effect
  • Scheduling
  • Single machine

ASJC Scopus subject areas

  • Control and Optimization

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