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Collaborative Multifidelity-Based Surrogate Models for Genetic Programming in Dynamic Flexible Job Shop Scheduling.

Fangfang Zhang, Yi Mei, Su Nguyen

    IEEE Transactions on Cybernetics
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    This study introduces a new multifidelity surrogate-assisted genetic programming (GP) approach to speed up dynamic flexible job shop scheduling (JSS). The method significantly reduces computational costs without compromising scheduling performance.

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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Computer Science

    Background:

    • Dynamic flexible job shop scheduling (JSS) presents significant challenges due to complex routing and sequencing decisions under unpredictable events.
    • Genetic programming (GP) is a powerful hyperheuristic for evolving JSS heuristics but suffers from computationally expensive simulation-based evaluations.
    • Improving the training efficiency of GP for JSS is crucial for practical applications.

    Purpose of the Study:

    • To propose a novel multifidelity-based surrogate-assisted genetic programming (GP) algorithm to enhance training efficiency for dynamic flexible job shop scheduling (JSS).
    • To develop an effective collaboration mechanism with knowledge transfer for leveraging multifidelity surrogate models in JSS.
    • To significantly reduce the computational cost associated with GP-based JSS without sacrificing performance.

    Main Methods:

    • Development of multifidelity surrogate models by simplifying the JSS problem.
    • Implementation of a knowledge transfer mechanism for effective collaboration between multifidelity surrogate models.
    • Integration of these components into a surrogate-assisted GP framework for JSS.
    • Empirical evaluation across six diverse JSS scenarios.

    Main Results:

    • The proposed multifidelity surrogate-assisted GP algorithm dramatically reduces computational costs in all tested scenarios.
    • Scheduling performance is maintained or improved compared to traditional GP methods.
    • With equivalent training time, the new algorithm achieves significantly better performance in most scenarios and comparable performance in others.

    Conclusions:

    • The multifidelity surrogate-assisted GP approach offers a computationally efficient and effective solution for dynamic flexible job shop scheduling.
    • This method addresses the high computational expense of traditional GP for JSS.
    • The proposed algorithm demonstrates superior or equivalent performance and efficiency across various JSS scenarios.