Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop

Danping Lin, Ka Man Lee, William Ho

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem.
Original languageEnglish
Pages (from-to)1282-1290
Number of pages9
JournalEngineering Applications of Artificial Intelligence
Volume26
Issue number4
DOIs
Publication statusPublished - 1 Apr 2013

Keywords

  • Genetic algorithm
  • Multi-level encoding
  • Re-entrant
  • Resource-constrained

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this