集装箱多式联运枢纽港作业设备调度优化

Optimization of Operation Equipment Scheduling for Container Intermodal Transport Hub Ports

摘要: 装卸设备作为集装箱多式联运枢纽的核心资源，其有效组织及协同作业与枢纽港降本增效、节能减排等目标均直接相关。以多式联运枢纽港集装箱中转作业过程为研究背景，将集装箱多式联运枢纽港作业涉及的龙门吊、正面吊、集卡、岸桥等设备协同调度问题作为研究对象，综合考虑设备间协同作业及节能减排需求，建立以总完工时间最小和能耗最低为目标的双目标优化数学模型，并使用改进遗传算法进行求解，缩小完工时间，降低能耗，得到较优设备协同调度方案。不同规模算例研究发现：针对多式联运港口装卸设备开展协同调度可在提升作业效率的同时降低能耗；改进遗传算法求解效率更高，缩短10%左右求解时间；且随着作业箱量的增加，改进算法对优化目标的提升程度更加显著。以上结果表明改进后的遗传算法具有更好地求解性能。

Abstract: As the core resource of container multimodal transportation hubs, the effective organization and collaborative operation of loading and unloading equipment are directly related to the goals of cost reduction, efficiency increase, energy conservation and emission reduction of hub ports. Taking the container transfer operation process of multimodal transport hub ports as the research background, the collaborative scheduling problem of gantry cranes, front cranes, trucks, and quay bridges involved in container multimodal transport hub port operations is taken as the research object. Taking into account the collaborative operation between equipment and energy conservation and emission reduction requirements, a dual objective optimization mathematical model is established with the goal of minimizing total completion time and energy consumption, and an improved genetic algorithm is used to solve the problem, so as to shorten the completion time, reduce energy consumption, and obtain an optimal equipment collaborative scheduling plan. Case studies on different scales have found that collaborative scheduling for multimodal port loading and unloading equipment can improve operational efficiency while reducing energy consumption; the improved genetic algorithm has higher solving efficiency and can reduce the solving time by about 10%. And as the number of job boxes increases, the improvement algorithm has a more significant impact on the optimization objectives. The above results indicate that the improved genetic algorithm has better solving performance.