GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Jia Luo, Shigeru Fujimura, Didier El Baz, Bastien Plazolles

Research output: Contribution to journalArticle

3 Citations (Scopus)


Due to new government legislation, customers’ environmental concerns and continuously rising cost of energy, energy efficiency is becoming an essential parameter of industrial manufacturing processes in recent years. Most efforts considering energy issues in scheduling problems have focused on static scheduling. But in fact, scheduling problems are dynamic in the real world with uncertain new arrival jobs after the execution time. This paper proposes an energy efficient dynamic flexible flow shop scheduling model using the peak power value with consideration of new arrival jobs. As the problem is strongly NP-hard, a priority based hybrid parallel Genetic Algorithm with a predictive reactive complete rescheduling strategy is developed. In order to achieve a speedup to meet the short response in the dynamic environment, the proposed method is designed to be highly consistent with the NVIDIA CUDA software model. Finally, numerical experiments are conducted and show that our approach can not only solve the problem flexibly, but also gain competitive results and reduce time requirements dramatically.

Original languageEnglish
JournalJournal of Parallel and Distributed Computing
Publication statusAccepted/In press - 2018 Jan 1



  • Dynamic scheduling
  • Energy efficiency
  • Flexible flow shop
  • GPU Computing
  • Hybrid parallel genetic algorithm

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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