An effective coding approach for multiobjective integrated resource selection and operation sequences problem

Haipeng Zhang, Mitsuo Gen, Yoonho Seo

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions.

Original languageEnglish
Pages (from-to)385-397
Number of pages13
JournalJournal of Intelligent Manufacturing
Volume17
Issue number4
DOIs
Publication statusPublished - 2006 Aug

Keywords

  • Integrated resource selection and operation sequences
  • Intelligent manufacturing system
  • Left-shift hillclimber
  • Multiple criteria model
  • Multistage operation-based genetic algorithm

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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