交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (6): 317-326.DOI: 10.16097/j.cnki.1009-6744.2025.06.029

• 系统工程理论与方法 • 上一篇    下一篇

自动化干散货码头资源分配与多机械协同调度优化研究

计明军* ，李嘉伟，胡寒霖，高振迪   

  1. 大连海事大学，交通运输工程学院，辽宁大连116026
  • 收稿日期:2025-04-21 修回日期:2025-07-28 接受日期:2025-09-18 出版日期:2025-12-25 发布日期:2025-12-24
  • 作者简介:计明军(1973—)，男，内蒙古赤峰人，教授。
  • 基金资助:
    国家自然科学基金 (72571038，72201045)。

Resource Allocation and Multi-machinery Cooperative Scheduling Optimization in Automated Dry Bulk Terminals

JI Mingjun*, LI Jiawei, HU Hanlin, GAO Zhendi   

  1. School of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Received:2025-04-21 Revised:2025-07-28 Accepted:2025-09-18 Online:2025-12-25 Published:2025-12-24
  • Supported by:
    National Natural Science Foundation of China (72571038，72201045)。

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摘要： 针对自动化干散货码头存在多种资源和机械设备导致多环节协同调度困难的问题，本文提出基于船舶在港时间最小和码头作业成本最小为目标的泊位-装船机-堆场协同调度模型。由于此问题具有大规模及非线性特征，在求解上具有挑战性，故提出一种基于灰狼优化的两阶段算法用于模型求解。第1阶段基于改进灰狼优化算法求解泊位-装船机分配方案，第2阶段基于分流机-堆场分配算法筛选符合作业线和堆场约束的可达方案。最后，以安徽长久内河码头数据为基础进行模型可行性和算法优越性验证。数值结果表明，本文建立的优化模型高度契合多资源和多机械协同作业的自动化干散货码头作业场景，符合实际作业约束，能充分利用码头资源和机械设备；本文提出的算法能有效求解该优化问题，较粒子群算法、遗传算法、灰狼优化算法、鲸鱼优化算法和哈里斯鹰优化算法在总成本的求解效果分别提升8.1%、8.7%、6.5%、2.4%和4.5%。

关键词: 水路运输, 资源分配, 灰狼优化算法, 自动化干散货码头

Abstract: This paper addresses the difficulties of multi-resource and multi-equipment coordination scheduling in automated dry bulk terminals by proposing a berth-ship loader-stockyard collaborative scheduling model with dual objectives of minimizing ship port time and terminal operation costs. Due to the large-scale and nonlinear characteristics of the problem which bring significant computational challenges, this paper proposes a two-stage algorithm based on grey wolf optimization for model solution. The first stage employs an improved algorithm of grey wolf optimization to solve the berth-ship loader allocation scheme, while the second stage uses an algorithm of distribution machine-stockyard allocation to screen feasible solutions that satisfy operational line and stockyard constraints. Finally, the feasibility and algorithm superiority of model are validated using the data from Anhui Changjiu Inland River Terminal. The numerical results demonstrate that the established optimization model is highly suitable for the operation scenarios in automated dry bulk terminal involving multi-resource and multi-equipment collaborative operations, which conforms to actual operational constraints, and can fully utilize terminal resources and mechanical equipment. The proposed algorithm effectively solves this optimization problem, achieving the solution improvements of 8.1%, 8.7%, 6.5%, 2.4%, and 4.5% in total cost through comparing with the algorithms of particle swarm optimization, genetic, grey wolf optimization, whale optimization, and Harris hawks optimization, respectively.

Key words: waterway transportation, resource allocation, grey wolf optimizer algorithm, automated dry bulk terminal

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