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Related Experiment Video

Updated: Apr 30, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Automatic programming via iterated local search for dynamic job shop scheduling.

Su Nguyen, Mengjie Zhang, Mark Johnston

    IEEE Transactions on Cybernetics
    |May 8, 2014
    PubMed
    Summary
    This summary is machine-generated.

    A new method, Automatic Programming via Iterated Local Search (APRILS), designs specialized dispatching rules for dynamic job shop scheduling. APRILS outperforms existing methods like tree-based genetic programming and gene expression programming in effectiveness and efficiency.

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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Manufacturing Systems

    Background:

    • Dispatching rules are crucial for sequencing and scheduling decisions in manufacturing.
    • No single dispatching rule is universally optimal due to system-specific characteristics.
    • Existing evolutionary computation methods (TGP, GEP) for automatic rule design have high computational costs and limited exploitation.

    Purpose of the Study:

    • To develop a novel approach for automatic design of specialized dispatching rules.
    • To address the limitations of computational cost and exploitation in existing methods.
    • To enhance scheduling performance in dynamic job shop environments.

    Main Methods:

    • Developed Automatic Programming via Iterated Local Search (APRILS).
    • APRILS utilizes multiple local searches initiated with modified programs from previous best solutions.
    • Employs a hybrid search mechanism balancing exploration and exploitation.

    Main Results:

    • APRILS demonstrated superior effectiveness and efficiency compared to Tree-Based Genetic Programming (TGP) and Gene Expression Programming (GEP).
    • Generated dispatching rules by APRILS were more compact than those from genetic programming.
    • APRILS achieved better performance across various simulation scenarios.

    Conclusions:

    • APRILS offers an effective and efficient approach for designing specialized dispatching rules.
    • The method's success is attributed to its balanced search mechanism.
    • APRILS provides a promising alternative for optimizing dynamic job shop scheduling.