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Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs.

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    Summary
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    This study introduces a hybrid artificial bee colony (ABC) algorithm to optimize a complex two-stage flow-shop problem with job deterioration and parallel batching. The novel approach enhances solution quality and population diversity for improved scheduling efficiency.

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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Manufacturing Systems Engineering

    Background:

    • The parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs presents significant scheduling challenges.
    • Existing methods may not adequately address the complexities of two-stage processes with both job deterioration and parallel batching constraints.

    Purpose of the Study:

    • To develop and evaluate a hybrid artificial bee colony (ABC) algorithm for solving the DFSP with deteriorating jobs.
    • To improve makespan and solution quality in a two-stage manufacturing environment.

    Main Methods:

    • A hybrid artificial bee colony (ABC) algorithm incorporating problem-specific heuristics (batch assignment, right-shifting).
    • Development of encoding/decoding strategies and five local search operators tailored for DFSP and parallel batching.
    • Introduction of a novel scout bee heuristic leveraging global and local best solutions for enhanced search.

    Main Results:

    • The proposed hybrid ABC algorithm demonstrates highly effective performance.
    • Significant improvements in makespan were achieved through specialized heuristics.
    • The algorithm favorably compares against existing methods in terms of solution quality and population diversity.

    Conclusions:

    • The hybrid ABC algorithm is a powerful tool for addressing the parallel batching DFSP with deteriorating jobs.
    • The integration of novel heuristics and search strategies enhances scheduling efficiency in complex manufacturing systems.
    • The proposed method offers a superior balance of solution quality and population diversity.