An integrated container scheduling problem (ICSP) is a significant challenge to improve the overall efficiency of (un)loading, transshipment, and reduce energy consumption in container terminals. In this study, we address a ICSP with two transshipment routes (ICSP_TR) for sea-road containers. First, a multi-objective optimization mathematical model is formulated for the ICSP_TR. The objectives are to minimize the maximum completion time, the total load waiting time of automatic guided vehicles (AGVs), and the total energy consumption of quay cranes (QCs) and yard cranes (YCs). Second, an improved non-dominated sorting genetic algorithm II (INSGAII) is proposed to solve the ICSP_TR. The order crossover and two-point mutation are employed for container sequence. A following rule is designed for transshipment routes and external trucks. The early complete time rule is adopted in equipment allocation to configure QCs, AGVs, and YCs. Third, an external archive technology and a variable neighborhood local search strategy are developed to improve the exploitation ability. Finally, 80 instances based on ICSP_TR and the single route ICSP are solved, respectively. Two storage situations of containers are compared between ICSP_TR and the single route ICSP. The results based on ICSP_TR show higher feasibility and effectiveness for hybrid transshipment. Furthermore, the results of the algorithm analysis show that INSGAII outperforms the original NSGAII and two other prominent multi-objective algorithms in convergence, diversity, and distribution for the ICSP_TR. Moreover, experimental results verify that INSGAII is capable of generating higher-quality scheduling schemes, offering container terminal managers a broader array of superior options.


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    Title :

    An Improved NSGAII for Integrated Container Scheduling Problems With Two Transshipment Routes


    Contributors:
    Zhong, Lingchong (author) / Li, Wenfeng (author) / Gao, Kaizhou (author) / He, Lijun (author) / Zhou, Yong (author)

    Published in:

    Publication date :

    2024-10-01


    Size :

    3250514 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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