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Multiobjective Optimization-Aided Decision-Making System for Large-Scale Manufacturing Planning.

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    This study introduces an interactive system for manufacturing planning (MP) to balance order fulfillment and costs. The novel two-stage multiobjective optimization algorithm (TSMOA) efficiently solves complex problems with reduced computation.

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    Area of Science:

    • Operations Research
    • Industrial Engineering
    • Computational Optimization

    Background:

    • Real-world manufacturing planning (MP) presents significant challenges due to numerous decision variables, conflicting objectives (maximizing order fulfillment, minimizing cost), and complex constraints.
    • Existing methods struggle with the scale and complexity of MP tasks, often requiring massive computational resources.
    • The need for efficient and practical solutions in manufacturing planning is critical for optimizing production and reducing costs.

    Purpose of the Study:

    • To develop an interactive multiobjective optimization-based system for real-world manufacturing planning (MP).
    • To address the dual objectives of maximizing order fulfillment rate and minimizing total cost in a complex manufacturing environment.
    • To introduce a novel algorithm that reduces computational burden while achieving satisfactory trade-offs.

    Main Methods:

    • The MP task is modeled as a multiobjective integer programming (MOIP) problem.
    • A two-stage multiobjective optimization algorithm (TSMOA) is employed to solve the MOIP problem.
    • TSMOA transforms the MOIP into a series of single-objective problems (SOPs), utilizing a new strategy with sequential SOPs for approximation and reduced computational cost.

    Main Results:

    • The developed MP system, incorporating TSMOA and the SOP solving strategy, demonstrates efficiency in real-world manufacturing planning applications.
    • TSMOA effectively handles the large number of decision variables and differing objective magnitudes inherent in MP tasks.
    • The algorithm's effectiveness is further validated on benchmark problems, confirming its robust performance.

    Conclusions:

    • The interactive multiobjective optimization-based MP system provides a promising solution for complex manufacturing planning challenges.
    • TSMOA offers an efficient approach to achieve satisfactory trade-offs between order fulfillment and cost minimization.
    • The system's ability to reduce computational requirements makes it a valuable tool for practical manufacturing decision-making.