4.7 Article

Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2021.3056143

关键词

Dynamic scheduling; Job shop scheduling; Sequential analysis; Processor scheduling; Routing; Genetic programming; Heuristic algorithms; Correlation coefficient; dynamic flexible job shop scheduling (JSS); genetic programming (GP); hyperheuristics; recombinative guidance

资金

  1. Marsden Fund of New Zealand Government [VUW1509, VUW1614]
  2. Science for Technological Innovation Challenge (SfTI) fund [E3603/2903]
  3. MBIE SSIF Fund [VUW RTVU1914]
  4. China Scholarship Council (CSC)/Victoria University Scholarship

向作者/读者索取更多资源

The article proposes a recombinative guidance mechanism to improve the quality of offspring in genetic programming, preserving promising building blocks from one parent and incorporating good building blocks from the other. This approach significantly outperforms state-of-the-art algorithms in terms of both final test performance and convergence speed across various scenarios.
Dynamic flexible job shop scheduling (JSS) is a challenging combinatorial optimization problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic environments. Genetic programming (GP), as a hyperheuristic approach, has been successfully used to evolve scheduling heuristics for dynamic flexible JSS. However, in traditional GP, recombination between parents may disrupt the beneficial building blocks by choosing the crossover points randomly. This article proposes a recombinative mechanism to provide guidance for GP to realize effective and adaptive recombination for parents to produce offspring. Specifically, we define a novel measure for the importance of each subtree of an individual, and the importance information is utilized to decide the crossover points. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building blocks of one parent and incorporating good building blocks from the other. The proposed algorithm is examined on six scenarios with different configurations. The results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms on most tested scenarios, in terms of both final test performance and convergence speed. In addition, the rules obtained by the proposed algorithm have good interpretability.

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