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Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm.

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    Summary
    This summary is machine-generated.

    This study introduces an improved Jaya algorithm for distributed flow-shop scheduling, optimizing job assignment and sequencing to minimize completion time and energy consumption. The enhanced algorithm demonstrates competitive performance in solving complex scheduling problems.

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    Area of Science:

    • Operations Research
    • Manufacturing Systems Engineering
    • Computational Intelligence

    Background:

    • Distributed flow-shop scheduling presents challenges in optimizing job assignment and sequencing across multiple factories.
    • Minimizing both makespan (completion time) and total energy consumption is crucial for efficient manufacturing operations.
    • Lot-streaming adds complexity by allowing jobs to be split and processed in stages.

    Purpose of the Study:

    • To develop a biobjective mathematical model for the distributed flow-shop scheduling problem with lot-streaming.
    • To propose an improved Jaya algorithm to efficiently solve this complex scheduling problem.
    • To enhance the Jaya algorithm's performance for makespan and energy efficiency.

    Main Methods:

    • Development of a biobjective mathematical model.
    • Implementation of an improved Jaya algorithm incorporating Nawaz-Enscore-Ham (NEH) initialization.
    • Design of specialized strategies for job-factory assignment, makespan minimization, and energy efficiency.

    Main Results:

    • The improved Jaya algorithm effectively addresses the distributed flow-shop scheduling problem with lot-streaming.
    • Experimental results on 120 instances validate the performance of the enhanced strategies.
    • The algorithm demonstrates high competitiveness in achieving optimal makespan and total energy consumption.

    Conclusions:

    • The proposed improved Jaya algorithm is a highly effective method for solving distributed flow-shop scheduling problems.
    • The integration of specific strategies significantly enhances the algorithm's performance for makespan and energy objectives.
    • This research contributes a competitive approach for optimizing complex manufacturing scheduling scenarios.