Solving a two-agent single-machine scheduling problem considering learning effect

Der Chiang Li, Peng Hsiang Hsu

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


Scheduling with multiple agents and learning effect has drawn much attention. In this paper, we investigate the job scheduling problem of two agents competing for the usage of a common single machine with learning effect. The objective is to minimize the total weighted completion time of both agents with the restriction that the makespan of either agent cannot exceed an upper bound. In order to solve this problem we develop several dominance properties and a lower bound based on a branch-and-bound to find the optimal algorithm, and derive genetic algorithm based procedures for finding near-optimal solutions. The performances of the proposed algorithms are evaluated and compared via computational experiments.

Original languageEnglish
Pages (from-to)1644-1651
Number of pages8
JournalComputers and Operations Research
Issue number7
Publication statusPublished - 2012 Jul

All Science Journal Classification (ASJC) codes

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


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