摘要
为解决铁路集装箱中心站内装卸设备间的高能效协同调度问题,首先分析集装箱装卸作业流程中的设备衔接情况,界定集装箱运输任务中装卸设备的不同作业状态,并分类量化其能源消耗;然后以装卸作业完工时间最短和总能耗最低为目标,构建综合考虑轨道门吊、集卡与正面吊协同作业过程的调度优化模型。为求解该模型,基于快速非支配排序遗传算法的核心思想,结合基于区域中心的贪婪变邻域搜索机制和微进化算法的优势基因保存机理,设计出混合多目标优化算法,并通过不同规模算例验证了模型与算法的可行性和有效性。结果表明,在较小的任务规模下,相较于传统遗传算法和快速非支配排序遗传算法,混合多目标优化算法的求解质量最优,其求解精度达到Gurobi求解器的96%,求解时间缩短92%;在大规模算例中,微进化机理和贪婪变邻域搜索机制的引入有效提高了混合多目标优化算法的寻优能力,提高了收敛效率,使其在求解速度与快速非支配排序遗传算法相近的前提下,求解质量提升约20%,且该优势会随着集装箱装卸任务量的增加而愈发显著。
To address the high-efficiency collaborative scheduling problem of loading and unloading equipment in railway container center stations,this paper first analyzes the equipment connection situation in the container loading and unloading operation process,defines the different operation states of loading and unloading equipment in container transportation tasks,and quantifies their energy consumption.Then,with the shortest completion time of loading and unloading operations and the lowest total energy consumption as the objectives,a scheduling optimization model that comprehensively considers the collaborative operation process of rail-mounted gantry cranes,container trucks,and reach stackers is constructed.To solve this model,a hybrid multi-objective optimization algorithm is designed based on the core idea of the fast non-dominated sorting genetic algorithm,combined with the advantages of the greedy variable neighborhood search mechanism based on regional centers and the gene preservation mechanism of the micro-evolution algorithm.The feasibility and effectiveness of the model and algorithm are verified through different-scale examples.The results show that,under a smaller task scale,compared with the traditional genetic algorithm and the fast non-dominated sorting genetic algorithm,the hybrid multi-objective optimization algorithm has the best solution quality,with a solution accuracy reaching 96%of the Gurobi solver and a solution time reduction of 92%.In large-scale examples,the introduction of the micro-evolution mechanism and the greedy variable neighborhood search mechanism effectively improves the optimization ability of the hybrid multi-objective optimization algorithm,accelerates the convergence efficiency,and makes its solution quality improve by about 20%under the premise of a solution speed similar to the fast non-dominated sorting genetic algorithm.Moreover,this advantage becomes more significant as the volume of container loading and unloading tasks increases.
作者
何世伟
刘逸凡
迟居尚
吴艺迪
赵日鑫
郭筱璇
HE Shiwei;LIU Yifan;CHI Jushang;WU Yidi;ZHAO Rixin;GUO Xiaoxuan(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China)
出处
《大连交通大学学报》
2025年第6期19-29,共11页
Journal of Dalian Jiaotong University
基金
中央高校基本科研业务费专项基金(2024JBZX038)
国铁集团项目(N2023X028)。
关键词
集装箱运输
协同调度
混合多目标优化算法
作业状态
能源消耗
铁路集装箱中心站
container transportation
collaborative scheduling
hybrid multi-objective optimization algorithm
job status
energy consumption
railway container terminal
