干散货港口卸船作业资源配置与流程优化研究

doi:10.16097/j.cnki.1009-6744.2026.02.029

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (2): 309-317.DOI: 10.16097/j.cnki.1009-6744.2026.02.029

干散货港口卸船作业资源配置与流程优化研究

李海江^a,b，赵家鹏^a,b，郭静怡^a,b，马千里^*a,b，贾鹏^a,b

大连海事大学，a.综合交通运输协同创新中心；b.航运经济与管理学院，辽宁大连116026

收稿日期:2025-08-28 修回日期:2025-12-31 接受日期:2026-03-19 出版日期:2026-04-25 发布日期:2026-04-21
作者简介:李海江（1991—），男，河北张家口人，副教授。
基金资助:
国家重点研发计划(2023YFB4302200)；中央高校基本科研业务费专项资金 (3132025309)。

Resource Allocation and Process Optimization of Unloading Operations in Dry Bulk Ports

LI Haijiang^a,b, ZHAO Jiapeng^a,b, GUO Jingyi^a,b, MA Qianli^*a,b, JIA Peng^a,b

a. Collaborative Innovation Center for Transport Studies; b. School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, Liaoning, China

Received:2025-08-28 Revised:2025-12-31 Accepted:2026-03-19 Online:2026-04-25 Published:2026-04-21
Supported by:
National Key R&D Program of China(2023YFB4302200)；Fundamental Research Funds for the Central Universities (3132025309)。

摘要/Abstract

摘要： 干散货港口卸船作业的优化研究对于提升港口作业效率意义重大。针对干散货港口泊位、卸船机、堆场货位、水平运输等全流程的资源配置与作业优化问题，本文构建一种基于图结构的散货港口作业要素表征方法，并设计以最小化作业成本为目标的两阶段混合整数规划模型。首先，在码头卸船作业阶段，提出一种“泊位-卸船机”协同配置模型，设计了集成异步调度策略的非支配遗传算法的联合求解方法。实验表明，该模型可显著降低卸船作业能耗，降幅达14.3%。其次，在堆场作业环节，充分考虑货物堆存期的不确定性和运输路径约束，构建一种“堆场货位分配-水平运输流程”联合调度模型，并提出一种集成动态掩码机制的深度确定性策略梯度求解算法。结果显示，该算法显著提升了求解速度，同时，作业时间成本降幅达21%，作业能耗成本降幅达26.4%。本文所提解决方案可以显著降低干散货全流程装卸作业成本。

关键词: 水路运输, 作业流程优化, 混合整数规划, 干散货港口, 深度确定性策略梯度算法

Abstract: The optimization of unloading operations in dry bulk ports is crucial for enhancing the overall efficiency of ports. To address the integrated resource allocation and the optimization of unloading operations through entire process of berths, unloaders, yard storage slots, and horizontal transportation, this paper proposes a graph-based representation method for port operational elements. A two-stage mixed-integer programming model is developed with the objective of minimizing the total operational cost. First, for the quayside unloading stage, a collaborative "berth-unloader" configuration model based on an asynchronous operation strategy is proposed. A joint solution method utilizing a Non-dominated Sorting Genetic Algorithm is designed. Experimental results demonstrate that this model significantly reduces the unloading energy consumption by 14.3% . Second, for the yard operations stage, a joint scheduling model for "storage slot allocation and horizontal transportation flow" is constructed, fully considering the uncertainty of cargo dwell time and transport path constraints. A solving algorithm integrating a dynamic masking mechanism is proposed within a Deep Deterministic Policy Gradient framework. Results show that this algorithm notably improves the solution speed, while reducing the operational time cost by 21% and the energy consumption cost by 26.4%. The proposed comprehensive solution can significantly reduce the total handling cost for the entire operational process in dry bulk ports.

Key words: waterway transportation, operational process optimization, mixed-integer programming, dry bulk port, deep deterministic policy gradient

中图分类号:

U691.31

李海江, 赵家鹏, 郭静怡, 马千里, 贾鹏. 干散货港口卸船作业资源配置与流程优化研究[J]. 交通运输系统工程与信息, 2026, 26(2): 309-317.

LI Haijiang, ZHAO Jiapeng, GUO Jingyi, MA Qianli, JIA Peng. Resource Allocation and Process Optimization of Unloading Operations in Dry Bulk Ports[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(2): 309-317.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2026.02.029

http://www.tseit.org.cn/CN/Y2026/V26/I2/309

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干散货港口卸船作业资源配置与流程优化研究

Resource Allocation and Process Optimization of Unloading Operations in Dry Bulk Ports

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