Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Jia Luo, Didier El Baz, Rui Xue, Jinglu Hu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Integrating energy savings into production efficiency is considered as one essential factor in modern industrial practice. A lot of research dealing with energy efficiency problems in the manufacturing process focuses solely on building a mathematical model within a static scenario. However, in the physical world shop scheduling problems are dynamic where unexpected events may lead to changes in the original schedule after the start time. This paper makes an investigation into minimizing the total tardiness, the total energy cost and the disruption to the original schedule in the job shop with new urgent arrival jobs. Because of the NP hardness of this problem, a dual heterogeneous island parallel genetic algorithm with the event driven strategy is developed. To reach a quick response in the dynamic scenario, the method we propose is made with a two-level parallelization where the lower level is appropriate for concurrent execution within GPUs or a multi-core CPU while codes from the two sides can be executed simultaneously at the upper level. In the end, numerical tests are implemented and display that the proposed approach can solve the problem efficiently. Meanwhile, the average results have been improved with a significant execution time decrease.

Original languageEnglish
Pages (from-to)119-134
Number of pages16
JournalFuture Generation Computer Systems
Volume108
DOIs
Publication statusPublished - 2020 Jul

Keywords

  • Dynamic scheduling
  • Energy efficiency
  • GPU computing
  • Job shop scheduling
  • Multi-core processing
  • Parallel genetic algorithm

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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