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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Surprisingly Popular-Based Adaptive Memetic Algorithm for Energy-Efficient Distributed Flexible Job Shop Scheduling.

Rui Li, Wenyin Gong, Ling Wang

    IEEE Transactions on Cybernetics
    |June 8, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel adaptive memetic algorithm (SPAMA) to optimize energy efficiency in distributed flexible job shop scheduling. SPAMA enhances makespan and reduces energy consumption by improving operator selection and scheduling strategies.

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    Area of Science:

    • Operations Research
    • Manufacturing Systems Engineering
    • Artificial Intelligence

    Background:

    • Distributed manufacturing is increasingly prevalent, necessitating efficient scheduling solutions.
    • Existing methods for the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) face limitations in operator efficiency, energy saving, and premature convergence.
    • Current adaptive operator selection models may overlook effective operators with lower initial weights.

    Purpose of the Study:

    • To address the limitations of existing algorithms for the EDFJSP.
    • To develop a novel algorithm, the surprisingly popular-based adaptive memetic algorithm (SPAMA), for simultaneous makespan and energy consumption minimization.
    • To enhance the performance of memetic algorithms in solving complex scheduling problems.

    Main Methods:

    • Proposed a surprisingly popular-based adaptive memetic algorithm (SPAMA) incorporating problem-based local search (LS) operators.
    • Introduced a surprisingly popular degree (SPD) feedback-based self-modifying operator selection model.
    • Implemented a full active scheduling decoding strategy for energy reduction.
    • Designed an elite strategy to balance global search and local search.

    Main Results:

    • SPAMA demonstrated superior performance compared to state-of-the-art algorithms on benchmark datasets (Mk and DP).
    • The proposed methods effectively improved convergence and maintained population diversity.
    • The SPD feedback mechanism successfully identified and utilized efficient operators, correcting for crowd-based decision-making biases.
    • The full active scheduling decoding significantly reduced energy consumption.

    Conclusions:

    • SPAMA offers a superior approach to solving the energy-efficient distributed flexible job shop scheduling problem.
    • The novel components of SPAMA, including the SPD operator selection and elite strategy, are effective in optimizing both makespan and energy consumption.
    • This research provides a valuable contribution to the field of intelligent manufacturing and scheduling optimization.