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A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption

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    This study introduces a two-phase meta-heuristic (TPM) to solve the multiobjective flexible job shop scheduling problem (FJSP) with an energy consumption threshold, minimizing makespan and tardiness. The proposed TPM effectively optimizes scheduling while adhering to energy constraints.

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    Area of Science:

    • Operations Research
    • Industrial Engineering
    • Computer Science

    Background:

    • The flexible job shop scheduling problem (FJSP) is a well-researched area.
    • Multiobjective FJSP incorporating energy consumption thresholds remains under-investigated.
    • Challenges include meeting energy constraints and determining optimal thresholds.

    Purpose of the Study:

    • To develop a novel algorithm for multiobjective FJSP with energy constraints.
    • To minimize makespan and total tardiness simultaneously.
    • To address the difficulty in pre-determining energy consumption thresholds.

    Main Methods:

    • A two-phase meta-heuristic (TPM) combining Imperialist Competitive Algorithm (ICA) and Variable Neighborhood Search (VNS).
    • Phase 1: ICA solves a converted FJSP including makespan, tardiness, and energy consumption.
    • Phase 2: VNS refines solutions for the original FJSP, incorporating an optimized energy threshold.

    Main Results:

    • The proposed TPM demonstrates strong performance on the considered FJSP.
    • Extensive experiments validate the algorithm's effectiveness.
    • TPM is identified as a competitive approach for this complex scheduling problem.

    Conclusions:

    • The developed TPM is a highly effective method for multiobjective FJSP with energy constraints.
    • The approach successfully balances makespan, tardiness, and energy consumption.
    • This research contributes a valuable tool for energy-aware production scheduling.