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Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop

Fangfang Zhang, Yi Mei, Su Nguyen

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    This study introduces advanced genetic programming (GP) algorithms for dynamic flexible job-shop scheduling (DFJSS), enhancing multitask multiobjective learning. The developed methods effectively learn scheduling heuristics, improving performance and maintaining solution quality for complex manufacturing challenges.

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    Area of Science:

    • Artificial Intelligence
    • Operations Research
    • Manufacturing Engineering

    Background:

    • Evolutionary multitask multiobjective learning is established but underutilized in dynamic combinatorial optimization.
    • Dynamic Flexible Job-Shop Scheduling (DFJSS) presents significant real-world manufacturing challenges.
    • Genetic Programming (GP) has been limited to single-objective DFJSS, struggling with heuristic space complexity.

    Purpose of the Study:

    • To adapt evolutionary multitask multiobjective learning for DFJSS.
    • To develop novel GP algorithms for handling multiple objectives in dynamic scheduling.
    • To enhance the learning of effective scheduling heuristics for DFJSS.

    Main Methods:

    • Proposed a multipopulation-based multitask multiobjective GP algorithm to maintain heuristic quality per task.
    • Developed a task-oriented knowledge-sharing strategy within the GP framework for improved heuristic learning.
    • Evaluated algorithm performance on DFJSS problems with multiple objectives.

    Main Results:

    • The multipopulation-based GP algorithms demonstrated good performance across all examined tasks.
    • The task-oriented knowledge-sharing strategy significantly improved the effectiveness of learning scheduling heuristics.
    • Maintained high quality and diversity of individuals within the GP populations for corresponding tasks.
    • Learned Pareto fronts indicated competitive scheduling heuristics for DFJSS objectives.

    Conclusions:

    • Multitask multiobjective GP, particularly with task-oriented knowledge sharing, is effective for DFJSS.
    • The proposed algorithms successfully address the challenges of learning heuristics in complex, dynamic, multiobjective environments.
    • This research advances the application of advanced evolutionary computation in manufacturing scheduling.