A Novel MOEA/D for multiobjective scheduling of flexible manufacturing systems

Xinnian Wang, Keyi Xing, Chao Bo Yan, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This paper considers the multiobjective scheduling of flexible manufacturing systems (FMSs). Due to high degrees of route flexibility and resource sharing, deadlocks often exhibit in FMSs. Manufacturing tasks cannot be finished if any deadlock appears. For solving such problem, this work develops a deadlock-free multiobjective evolutionary algorithm based on decomposition (DMOEA/D). It intends to minimize three objective functions, i.e., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. A type of a discrete differential evolution (DDE) algorithm is also developed for solving each subproblem. The mutation operator of the proposed DDE is based on the hamming distance of two randomly selected solutions, while the crossover operator is based on Generalization of Order Crossover. Experimental results demonstrate that the proposed DMOEA/D can significantly outperform a Pareto domination-based algorithm DNSGA-II for both 2-objective and 3-objective problems on the studied FMSs.

Original languageEnglish (US)
Article number5734149
JournalComplexity
Volume2019
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • General

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