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Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications

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  • Lin Lin
  • Mitsuo Gen

Abstract

Evolutionary Algorithms (EAs) has attracted significantly attention with respect to complexity scheduling problems, which is referred to evolutionary scheduling. However, EAs differ in the implementation details and the nature of the particular scheduling problem applied. In order to have an effective implementation of EAs for production scheduling, this paper focuses on making a survey of researches based on using hybrid EAs. Starting from scheduling description, we identify the classification and graph representation of scheduling problems. Then, we present the various representations, hybridisation techniques and machine-learning techniques to enhancing EAs. Finally, we also present successful applications in manufacturing.

Suggested Citation

  • Lin Lin & Mitsuo Gen, 2018. "Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 193-223, January.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:1-2:p:193-223
    DOI: 10.1080/00207543.2018.1437288
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    Cited by:

    1. Lu Sun & Lin Lin & Haojie Li & Mitsuo Gen, 2019. "Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling," Mathematics, MDPI, vol. 7(4), pages 1-20, March.
    2. Mohamed Kriouich & Hicham Sarir, 2024. "Artificial Intelligence Application in Production Scheduling Problem Systematic Literature Review: Bibliometric Analysis, Research Trend, and Knowledge Taxonomy," SN Operations Research Forum, Springer, vol. 5(2), pages 1-24, June.

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