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A Two-Stage Multiobjective Evolutionary Algorithm for Multiobjective Multidepot Vehicle Routing Problem With Time

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    This study introduces a new algorithm for the complex vehicle routing problem with time windows. The proposed method enhances solution diversity and convergence, outperforming existing approaches.

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    Area of Science:

    • Operations Research
    • Computer Science
    • Logistics Management

    Background:

    • The multiobjective multidepot vehicle routing problem with time windows (MOMDVRP-TW) presents significant logistical challenges.
    • Existing algorithms often struggle to balance solution convergence and diversity for complex routing problems.

    Purpose of the Study:

    • To propose a novel algorithm for solving the MOMDVRP-TW.
    • To introduce a new strategy for balancing convergence and diversity in evolutionary algorithms.
    • To design and test new real-world instances for the MOMDVRP-TW.

    Main Methods:

    • Development of a two-stage multiobjective evolutionary algorithm (TS-MOEA).
    • Stage I: Focuses on finding extreme solutions to form an initial Pareto front.
    • Stage II: Extends extreme solutions to approximate the complete Pareto front, incorporating a hybrid neighborhood structure for solution improvement.

    Main Results:

    • The TS-MOEA effectively balances convergence and diversity.
    • Experimental results demonstrate that TS-MOEA significantly outperforms two other representative algorithms.
    • The proposed algorithm provides a robust method for tackling complex vehicle routing scenarios.

    Conclusions:

    • The TS-MOEA offers a superior approach to solving the MOMDVRP-TW.
    • The two-stage strategy is effective in approximating the Pareto front.
    • This research contributes a valuable tool for optimizing vehicle routing and logistics operations.