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Multiobjective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows: Formulation,

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    This study introduces a multiobjective vehicle routing problem with simultaneous delivery and pickup and time windows (MO-VRPSDPTW) for logistics. Multiobjective local search (MOLS) generally outperforms multiobjective memetic algorithm (MOMA) on these complex routing problems.

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    Area of Science:

    • Operations Research
    • Logistics and Supply Chain Management
    • Computational Intelligence

    Background:

    • The vehicle routing problem with simultaneous delivery and pickup and time windows (VRPSDPTW) is a critical challenge in modern logistics and closed-loop supply chains.
    • Real-world VRPSDPTW applications often involve multiple, conflicting objectives, necessitating multiobjective optimization approaches.
    • Existing benchmarks may not fully capture the complexity of practical, multiobjective VRPSDPTW scenarios.

    Purpose of the Study:

    • To define and address a general multiobjective VRPSDPTW (MO-VRPSDPTW) with five objectives.
    • To introduce a new set of realistic MO-VRPSDPTW instances derived from real-world data.
    • To compare the performance of two metaheuristics, multiobjective local search (MOLS) and multiobjective memetic algorithm (MOMA), for solving MO-VRPSDPTW.

    Main Methods:

    • Definition of a general multiobjective VRPSDPTW (MO-VRPSDPTW) formulation.
    • Generation of novel MO-VRPSDPTW instances using real-world data to enhance realism and difficulty.
    • Implementation and comparative analysis of Multiobjective Local Search (MOLS) and Multiobjective Memetic Algorithm (MOMA).

    Main Results:

    • MOLS demonstrated superior performance compared to MOMA across a majority of tested instances.
    • The performance advantage of MOLS over MOMA was less pronounced on the newly introduced real-world instances.
    • The proposed real-world instances present a more challenging testbed for MO-VRPSDPTW algorithms.

    Conclusions:

    • MOLS is a highly effective algorithm for solving MO-VRPSDPTW, particularly on traditional benchmark instances.
    • The developed real-world instances highlight the need for robust algorithms capable of handling complex, practical routing scenarios.
    • Further research may be needed to refine MOMA or explore hybrid approaches for improved performance on realistic MO-VRPSDPTW problems.