An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop

Xinyu Li, Liang Gao, Quanke Pan, Liang Wan, Kuo-Ming Chao

Research output: Contribution to journalArticle

19 Citations (Scopus)
78 Downloads (Pure)

Abstract

Process planning and scheduling are modeled sequentially in the traditional manufacturing system. However, because of their complementarity, the increasing need to integrate them has emerged to enhance the manufacturing productivity significantly. Therefore, the integrated process planning and scheduling (IPPS) is becoming a hotspot in providing a blueprint for efficient manufacturing system. This paper proposes a novel algorithm hybridizing the genetic algorithm with strong global searching ability and variable neighborhood search with strong local searching ability for the IPPS problem. To improve the searching ability, a novel procedure, encoding method, and local search method have been designed. Effective operators have been adopted. Three experiments with totally 37 well-known benchmark problems are employed to evaluate the performance of the proposed method. Based on the results, the proposed algorithm outperforms the state-of-the-art methods and finds the new solutions (the best solutions found so far) for some problems. The proposed method has also been applied on a real-world case from a nonstandard equipment production workshop for the packaging machine of a machine tool company in China. The solution demonstrates that it can solve real-world cases very well.
Original languageEnglish
Pages (from-to)(In-press)
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume(In-press)
DOIs
Publication statusPublished - 10 Dec 2018

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Keywords

  • Hybrid algorithm
  • Integrated process planning and scheduling (IPPS)
  • Variable neighborhood search (VNS)

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