A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization

Han Wang, Zhigang Jiang, Yan Wang, Hua Zhang, Yanhong Wang

Research output: Contribution to journalArticle

Abstract

Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving.

Original languageEnglish
Pages (from-to)575-588
Number of pages14
JournalJournal of Cleaner Production
Volume188
DOIs
Publication statusPublished - 27 Mar 2018

Keywords

  • Energy consumption
  • Energy-saving scheduling
  • Flexible job-shop scheduling problem
  • Modified genetic algorithm
  • Particle swarm optimization

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