The significance of maritime transportation highlights the need to enhance the efficiency of container terminals. This study addresses a challenge within maritime transportation, specifically the continuous berth allocation and time-variant quay crane assignment problem (C/T-V BACAP). We formulate a comprehensive mathematical model of C/T-V BACAP. To solve the problem, we propose an effective memetic algorithm with a heuristic decoding method, named HMA, which comprises three essential components: a three-stage heuristic decoding method, a clustering-based evolutionary strategy, and a target-guided local search operator. The three-stage heuristic decoding method guarantees solution feasibility and high quality through the entire optimization, allowing the following strategies to fully utilize their search capabilities. The clustering-based evolutionary strategy refines the search space and diversifies the promising candidates. Meanwhile, the target-guided local search operator rapidly optimizes the allocation for the challenging vessel. The experimental results demonstrate that the proposed algorithm delivers excellent performance, especially in handling large-scale instances (up to 60 vessels). Our proposed method outperforms the state-of-the-art BACAP algorithms by an average margin of 150% in terms of berth offset and waiting time in most problem instances.


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    Title :

    Continuous Berth Allocation and Time-Variant Quay Crane Assignment: Memetic Algorithm With a Heuristic Decoding Method


    Contributors:
    Xu, Li-Sha (author) / Huang, Ting (author) / Zhao, Bo-Wen (author) / Gong, Yue-Jiao (author) / Liu, Jing (author)


    Publication date :

    2025-03-01


    Size :

    2908591 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English