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作 者:王小寒 靳志宏 WANG Xiaohan;JIN Zhihong(College of Transportation Engineering,Dalian Maritime University,Dalian 116026,Liaoning,China)
机构地区:[1]大连海事大学,交通运输工程学院,辽宁大连116026
出 处:《交通运输系统工程与信息》2024年第5期283-294,共12页Journal of Transportation Systems Engineering and Information Technology
基 金:国家自然科学基金(72172023);辽宁省教育厅基本科研项目(LJKMR20220378)。
摘 要:海铁联运港站共享堆场前提下,海铁联运箱可堆存在港口堆场和铁路堆场,也可实现跨堆场箱区间的转场,不同堆存方案下载运设备作业和箱区利用存在明显差异。为充分利用海铁联运港站堆场空间资源,本文基于共享堆场,考虑进出口双向货流,以海铁联运集装箱在港中转成本最小化为目标,构建非线性整数规划模型,决策箱区分配及箱区转场问题。针对问题自身特点,为自适应平衡种群多样性和全局收敛性,设计基于Q学习的协同进化遗传算法求解问题。通过不同规模的算例验证模型与算法的有效性。结果表明,本文提出的优化算法具备更好的收敛精度。相较于其他3种传统堆存形式,“共享—转场”堆存形式能有效地均衡转场成本和装卸效率,总转运成本平均降低了15.17%,11.96%,15.09%。最后,通过多个单位成本进行灵敏度分析,以验证算法的普适性。Sea-rail intermodal container terminals facilitate shared yards,allowing sea-rail intermodal containers to be stored in blocks at port and railway container terminals,and containers to be transferred between different yard blocks.There are significant differences in the operation of equipment and block utilization between different storage systems.To effectively utilize space resources at sea-rail intermodal container terminals,the joint optimization of block allocation and transfer is determined by considering import and export container flows based on shared yards.An integer nonlinear programming model is developed to minimize the total transshipment costs.Based on the characteristics of the problem,a co-evolutionary genetic algorithm is designed to adaptively balance population diversity and global convergence by incorporating Q-learning principles to obtain solutions.Various numerical experiments are conducted to verify the effectiveness of the model and the algorithm.The results show that the proposed algorithm has better convergence accuracy.Compared with the other three traditional storage forms,the proposed"shared and transferred"form can effectively balance the transfer cost and handling efficiency,resulting in an average reduction of 15.17%,11.96%,and 15.09%in the total handling cost.Finally,a sensitivity analysis to multiple unit costs is performed to verify the universality of the algorithm.
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