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Operation of the Collaborative Composite Manufacturing CCM System
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Learning-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop With Sequencing Flexibility.

ChengRan Lin, ZhengCai Cao, MengChu Zhou

    IEEE Transactions on Cybernetics
    |November 10, 2022
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    Summary

    A new learning-based cuckoo search (LCS) algorithm enhances scheduling for complex manufacturing jobs. This approach uses autoencoders and factorization machines for reliable, high-quality job-shop scheduling solutions.

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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Manufacturing Systems Engineering

    Background:

    • The flexible job-shop problem (FJSP) in postprinting and semiconductor manufacturing requires directed acyclic graphs for operation precedences, complicating traditional linear scheduling.
    • Existing optimization methods struggle with the high-dimensional solution space and complex dependencies in extended FJSP.

    Purpose of the Study:

    • To develop a reliable and high-quality scheduling algorithm for an extended flexible job-shop problem within a reasonable timeframe.
    • To introduce a novel learning-based cuckoo search (LCS) algorithm integrating deep learning and machine learning techniques for combinatorial optimization.

    Main Methods:

    • A sparse autoencoder is employed for dimensionality reduction of high-dimensional solutions, extending autoencoder applications to combinatorial optimization via an improved one-hot encoding method.
    • A factorization machine (FM) is utilized to capture feature interactions within the population, enhancing exploration capabilities.
    • A parallel framework with three co-evolved subpopulations (autoencoder-embedded, FM-assisted, and regular) is constructed, adaptively managed by a reinforcement learning algorithm.

    Main Results:

    • The proposed LCS algorithm demonstrated superior performance compared to CPLEX, classical heuristics, and recent methods in numerical simulations.
    • The integration of sparse autoencoders and factorization machines effectively addressed the challenges of high-dimensional solution spaces and complex variable interdependencies.
    • The reinforcement learning-based adaptive control balanced exploration and exploitation, optimizing the scheduling process.

    Conclusions:

    • The learning-based cuckoo search (LCS) algorithm provides a robust and effective solution for the extended flexible job-shop problem in advanced manufacturing environments.
    • The novel combination of autoencoders, factorization machines, and reinforcement learning offers a significant advancement in scheduling optimization techniques.
    • The LCS algorithm achieves high-quality and reliable schedules efficiently, outperforming existing state-of-the-art methods.