A meta-heuristic for minimizing total weighted flow time on parallel batch machines

Zhao hong Jia, Han Zhang, Wen tao Long, Joseph Y.T. Leung, Kai Li, Wei Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

To address the problem of minimizing the total weighted completion time on parallel batch processing machines with identical machine capacities, non-identical job sizes and unequal weights, an effective meta-heuristic based on ant colony optimization is proposed. After presenting a mathematic model of the problem, we provide an algorithm to calculate the lower bound. Then, a meta-heuristic is proposed to solve the problem. The heuristic information is defined with consideration of job weights and job sizes. Meanwhile, a candidate set for constructing the solution is used to narrow the search space. Additionally, to improve the solution quality, a local optimization strategy is incorporated. Simulation results show that the proposed algorithm is able to obtain a high-quality solution within a reasonable time, and outperforms the compared algorithms.

Original languageEnglish (US)
Pages (from-to)298-308
Number of pages11
JournalComputers and Industrial Engineering
Volume125
DOIs
StatePublished - Nov 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Keywords

  • Ant colony optimization algorithm
  • Non-identical job sizes
  • Parallel scheduling
  • Patch processing machines
  • Total weighted completion time

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