A two-agent single-machine scheduling problem with truncated sum-of-processing-times-based learning considerations

Edwin Tai Chiu Cheng, Shuenn Ren Cheng, Wen Hung Wu, Peng Hsiang Hsu, Chin Chia Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

83 Citations (Scopus)

Abstract

Scheduling with learning effects has received a lot of research attention lately. By learning effect, we mean that job processing times can be shortened through the repeated processing of similar tasks. On the other hand, different entities (agents) interact to perform their respective tasks, negotiating among one another for the usage of common resources over time. However, research in the multi-agent setting is relatively limited. Meanwhile, the actual processing time of a job under an uncontrolled learning effect will drop to zero precipitously as the number of jobs increases or a job with a long processing time exists. Motivated by these observations, we consider a two-agent scheduling problem in which the actual processing time of a job in a schedule is a function of the sum-of-processing-times-based learning and a control parameter of the learning function. The objective is to minimize the total weighted completion time of the jobs of the first agent with the restriction that no tardy job is allowed for the second agent. 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)534-541
Number of pages8
JournalComputers and Industrial Engineering
Volume60
Issue number4
DOIs
Publication statusPublished - 1 May 2011

Keywords

  • Scheduling
  • Simulated annealing
  • Truncated sum-of-processing-times- based learning
  • Two-agent

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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