Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities

Zhaohong Jia, Jianhai Yan, Joseph Y.T. Leung, Kai Li, Huaping Chen

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

We study the problem of scheduling on parallel batch processing machines with different capacities under a fuzzy environment to minimize the makespan. The jobs have non-identical sizes and fuzzy processing times. After constructing a mathematical model of the problem, we propose a fuzzy ant colony optimization (FACO) algorithm. Based on the machine capacity constraint, two candidate job lists are adopted to select the jobs for building the batches. Moreover, based on the unoccupied space of the solution, heuristic information is designed for each candidate list to guide the ants. In addition, a fuzzy local optimization algorithm is incorporated to improve the solution quality. Finally, the proposed algorithm is compared with several state-of-the-art algorithms through extensive simulated experiments and statistical tests. The comparative results indicate that the proposed algorithm can find better solutions within reasonable time than all the other compared algorithms.

Original languageEnglish (US)
Pages (from-to)548-561
Number of pages14
JournalApplied Soft Computing Journal
Volume75
DOIs
StatePublished - Feb 2019

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Fuzzy ant colony optimization algorithm
  • Fuzzy job processing time
  • Non-identical machine capacities
  • Parallel batch processing machines

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