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    Area of Science:

    • Operations Research
    • Industrial Engineering
    • Computer Science

    Background:

    • The job-shop scheduling problem (JSSP) is critical for factory efficiency and cost management.
    • Traditional JSSP models often focus solely on job completion time.
    • Real-world factories require optimization of multiple objectives, including time and cost.

    Purpose of the Study:

    • To propose the first many-objective JSSP model incorporating five practical objectives: completion time, total tardiness, advance time, production cost, and machine loss.
    • To develop a novel optimization approach for this complex, multi-objective problem.
    • To enhance the practicality of JSSP models for industrial applications.

    Main Methods:

    • A novel Multiple Populations for Multiple Objectives (MPMO) framework-based genetic algorithm (GA) named MPMOGA is proposed.
    • MPMO utilizes five distinct populations, each targeting one of the five objectives.
    • An Archive Sharing Technique (AST) and an Archive Update Strategy (AUS) are introduced to facilitate information exchange between populations and improve solution quality.

    Main Results:

    • MPMO effectively optimizes the five JSSP objectives simultaneously.
    • The AST and AUS contribute to approximating diverse regions of the Pareto front.
    • MPMO demonstrates superior performance compared to state-of-the-art algorithms on widely used test instances.

    Conclusions:

    • The proposed many-objective JSSP model offers a more practical representation of factory demands.
    • MPMO provides an effective approach for simultaneously optimizing multiple, often conflicting, objectives in JSSP.
    • MPMOGA represents a significant advancement in solving complex, multi-objective scheduling problems.