考虑能耗的海铁联运集装箱码头多设备协同调度

doi:10.19678/j.issn.1000-3428.0068369

摘要/Abstract

摘要：

随着多式联运和绿色港口的发展, 提高海铁联运效率和降低能源消耗成为码头亟需解决的问题。针对集装箱码头铁路作业区与码头作业区共堆场布局下设备利用率低与作业能耗高的问题, 构建混合装卸作业下轨道吊、自动导向车(AGV)及场桥的协同调度优化模型, 旨在实现作业完工时间最短、轨道吊和AGV的作业能耗最优, 以及提高轨道吊协同作业的利用率。该模型综合考虑轨道吊间相互干涉、AGV伴侣容量限制以及AGV和轨道吊装卸过程中的重载、空载与等待状态下的不同能耗等约束, 并针对轨道吊的干涉约束引入轨道吊任务分配策略。同时, 采用改进的混合灰狼遗传算法对模型进行求解, 具体改进策略包括引入灰狼算法位置更新策略优化遗传算法的交叉方式以提高最优解搜索效率, 以及在遗传算法的选择步骤前引入基于奖励机制的评估器增强算法的局部搜索能力。实验结果表明, 与轨道吊作业范围已知相比, 考虑轨道吊任务分配使轨道吊的平均利用率提升了3%~8%。与自适应混沌遗传算法、遗传算法相比, 改进混合灰狼遗传算法能在较短的作业完工时间内节约更多的能耗。

关键词: 海铁联运, 协同调度, 混合装卸, 任务分配, 改进混合灰狼遗传算法

Abstract:

With the growth of multimodal transportation and the push for green ports, improving the efficiency of sea-rail intermodal transportation and reducing energy consumption have become critical concerns for ports. This study focused on optimizing the layout of shared yards between the railway and ter\min al operation areas at a container ter\min al. The objective is to \min imize operation completion time and optimize the energy consumption of Rail-Mounted Gantry (RMG) cranes and Automatic Guided Vehicles (AGV), thereby improving the utilization rate of RMGs. To achieve this, a coordinated scheduling optimization model is developed for RMGs, AGVs, and yard cranes within a mixed loading and unloading environment. The model considered interference and task allocation among the RMGs, capacity constraints of AGV partners, and energy consumption variations during different AGV states such as loading, unloading, and waiting. In addition, a task assignment strategy that considered interference constraints is introduced for RMG operations. An improved hybrid grey wolf genetic algorithm is proposed to solve the model. This algorithm incorporated a grey wolf algorithm position update strategy to enhance the crossover method of the genetic algorithm, thereby improving the efficiency of finding optimal solutions. A reward-based evaluator is also introduced before the selection step of the genetic algorithm to enhance the local search capability of the algorithm. Experimental results indicated that considering task allocation for RMGs improved their average utilization rate by 3%-8% compared to scenarios where the RMG operating range is predeter\min ed. Furthermore, the improved hybrid grey wolf genetic algorithm reduced energy consumption more effectively within a shorter completion time compared to adaptive chaotic and traditional genetic algorithms. In conclusion, this study provides an effective and superior solution for improving efficiency and reducing energy consumption in sea-rail intermodal transportation at container ter\min al.

Key words: sea-rail intermodal transportation, coordinated scheduling, mixed loading and unloading, task assignment, improved hybrid gray wolf genetic algorithm

杨佳珠, 余芳, 杨勇生. 考虑能耗的海铁联运集装箱码头多设备协同调度[J]. 计算机工程, 2024, 50(10): 393-404.

YANG Jiazhu, YU Fang, YANG Yongsheng. Multi-Equipment Coordinated Scheduling Considering Energy Consumption in Sea-Rail Intermodal Container Ter\min al[J]. Computer Engineering, 2024, 50(10): 393-404.

https://www.ecice06.com/CN/Y2024/V50/I10/393

图/表 16

图1 海铁联运共堆场交接布局

Fig.1 Common yard handover layout for sea-rail transportation

图2 HGWGA算法流程

Fig.2 The procedure of HGWGA algorithm

图3 染色体编码

Fig.3 Chromosome code

图4 任务分配流程

Fig.4 Task allocation procedure

图5 染色体交叉

Fig.5 Chromosome crossover

图6 染色体变异

Fig.6 Chromosome mutation

图7 不同权重下完工时间与作业能耗情况

Fig.7 Completion time and operational energy consumption under different weights

图8 轨道吊作业路线

Fig.8 Route for rail crane operations

图9 两种情况下的轨道吊平均利用率对比

Fig.9 Comparison of the average utilization rate of rail cranes in the two case

图10 改进策略的算法收敛对比

Fig.10 Comparison of convergence of algorithms with improved strategies

图11 3种算法的15组实验性能趋势图

Fig.11 Performance trend plots for 15 sets of experiments for the three algorithms

图12 3种算法收敛曲线对比

Fig.12 Comparison of convergence curves of three algorithms

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