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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Many-Objective Evolutionary Algorithm With Reference Point-Based Fuzzy Correlation Entropy for Energy-Efficient Job

Wenfeng Li, Lijun He, Yulian Cao

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    Manufacturing factories face labor shortages. This study introduces novel algorithms for energy-efficient job-shop scheduling with multiskilled workers, optimizing makespan, tardiness, idle time, cost, and energy consumption.

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    Area of Science:

    • Operations Research
    • Industrial Engineering
    • Artificial Intelligence

    Background:

    • COVID-19 pandemic has caused significant labor shortages and social alienation in global manufacturing.
    • Improving production performance under limited labor resources is a critical challenge for factories worldwide.
    • Job-shop scheduling problems are complex, especially when considering energy efficiency and multiple objectives.

    Purpose of the Study:

    • To develop an energy-efficient job-shop scheduling model addressing limited, multiskilled labor.
    • To propose a many-objective optimization model with five key objectives: makespan, total tardiness, total idle time, total worker cost, and total energy consumption.
    • To introduce novel algorithms for solving this many-objective optimization problem (MaOP).

    Main Methods:

    • A many-objective optimization model was formulated with five objectives.
    • A novel fitness evaluation mechanism (FEM) based on fuzzy correlation entropy (FCE) was adopted.
    • An environmental selection mechanism (ESM) integrating FCE and clustering methods was proposed.
    • Two many-objective evolutionary algorithms incorporating the proposed FEM and ESM were developed.

    Main Results:

    • The effectiveness of the FCE-based FEM and ESM was experimentally verified.
    • The proposed algorithms demonstrated competitive performance when compared to four well-known peer algorithms.
    • The developed algorithms successfully addressed the energy-efficient job-shop scheduling problem with multiple objectives.

    Conclusions:

    • The novel algorithms offer a promising approach to tackle complex, many-objective scheduling problems in manufacturing.
    • The integration of fuzzy correlation entropy and clustering enhances the balance between solution convergence and diversity.
    • The study provides valuable insights for optimizing production under resource constraints and energy efficiency goals.