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Updated: Jan 19, 2026

Evolutionary History of Life on Earth
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People-Centric Evolutionary System for Dynamic Production Scheduling.

Su Nguyen, Mengjie Zhang, Damminda Alahakoon

    IEEE Transactions on Cybernetics
    |September 9, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a new people-centric evolutionary system for dynamic production scheduling. It enhances genetic programming (GP) for more effective and compact scheduling heuristics, allowing decision-maker input.

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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Manufacturing Systems Engineering

    Background:

    • Evolving production scheduling heuristics is complex due to dynamic environments and interdependent decisions.
    • Existing genetic programming (GP) methods show promise but struggle with difficult problems and lack decision-maker integration.
    • There is a need for systematic approaches to incorporate human expertise into evolutionary scheduling processes.

    Purpose of the Study:

    • To develop a novel people-centric evolutionary system for dynamic production scheduling.
    • To improve the discovery of powerful and compact scheduling heuristics.
    • To enable decision-makers to intervene and guide the evolutionary process.

    Main Methods:

    • Developed a novel people-centric evolutionary system for dynamic production scheduling.
    • Implemented a new mapping technique for incremental monitoring of the evolutionary process.
    • Introduced a new adaptive surrogate model to enhance the efficiency of genetic programming (GP).

    Main Results:

    • The proposed system significantly outperforms existing algorithms in dynamic flexible job shop scheduling.
    • Achieved superior scheduling performance and smaller heuristic sizes compared to current methods.
    • Demonstrated the system's ability to allow on-the-fly interaction for guiding evolution.

    Conclusions:

    • The novel people-centric evolutionary system offers a more effective approach to evolving production scheduling heuristics.
    • The system enhances GP efficiency and produces more powerful, compact heuristics.
    • Facilitates decision-maker intervention, leading to tailored and improved scheduling solutions.