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A Semantic Genetic Programming Approach to Evolving Heuristics for Multi-objective Dynamic Scheduling

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AI 2023: Advances in Artificial Intelligence (AI 2023)

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Abstract

Multi-objective dynamic flexible job shop scheduling (MO-DFJSS) is a challenging problem that requires finding high-quality schedules for jobs in a dynamic and flexible manufacturing environment, considering multiple potentially conflicting objectives simultaneously. A good approach to MO-DFJSS is to combine Genetic Programming (GP) with Non-dominated Sorting Genetic Algorithm II (NSGA-II), namely NSGP-II, to evolve a set of non-dominated scheduling heuristics. However, a limitation of NSGPII is that individuals with different genotypes can exhibit the same behaviour, resulting in a loss of population diversity. Semantic genetic programming (SGP) considers individual semantics during the evolutionary process and can enhance population diversity in various domains. However, its application in the domain of MO-DFJSS remains unexplored. Therefore, it is worthy to incorporate semantic information with NSGPII for MO-DFJSS. This study focuses on semantic diversity and semantic similarity. The results demonstrate that NSGPII considering semantic diversity yields better performance compared with the original NSGPII. Moreover, NSGPII incorporating semantic similarity achieves even better performance, highlighting the importance of maintaining a reasonable semantic distance between offspring and their parents. Further analysis reveals that the improved performance achieved by the proposed methods is attributed to the attainment of a more semantically diverse population through effective control of semantic distances between individuals.

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Authors and Affiliations

  1. Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, 6140, New Zealand

    Meng Xu, Yi Mei, Fangfang Zhang & Mengjie Zhang

Authors
  1. Meng Xu
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  2. Yi Mei
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  3. Fangfang Zhang
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  4. Mengjie Zhang
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Corresponding author

Correspondence to Fangfang Zhang .

Editor information

Editors and Affiliations

  1. The University of Sydney, Darlington, NSW, Australia

    Tongliang Liu

  2. Monash University, Clayton, VIC, Australia

    Geoff Webb

  3. The University of Newcastle, Callaghan, NSW, Australia

    Lin Yue

  4. CSIRO Data61, Sydney, NSW, Australia

    Dadong Wang

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Xu, M., Mei, Y., Zhang, F., Zhang, M. (2024). A Semantic Genetic Programming Approach to Evolving Heuristics for Multi-objective Dynamic Scheduling. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14472. Springer, Singapore. https://doi.org/10.1007/978-981-99-8391-9_32

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  • DOI: https://doi.org/10.1007/978-981-99-8391-9_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8390-2

  • Online ISBN: 978-981-99-8391-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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