计算机工程与应用 ›› 2022, Vol. 58 ›› Issue (10): 263-275.DOI: 10.3778/j.issn.1002-8331.2104-0337

• 工程与应用 • 上一篇    下一篇

集装箱码头设备配置与作业调度集成优化研究

仲昭林,孔珊,张纪会,郭乙运   

  1. 1.青岛大学 复杂性科学研究所,山东 青岛 266071
    2.山东省工业控制技术重点实验室,山东 青岛 266071
    3.青岛港国际股份有限公司,山东 青岛 266011
  • 出版日期:2022-05-15 发布日期:2022-05-15

Integrated Optimization of Container Terminal Equipment Configuration and Scheduling

ZHONG Zhaolin, KONG Shan, ZHANG Jihui, GUO Yiyun   

  1. 1.Institute of Complexity Science, Qingdao University, Qingdao, Shandong 266071, China
    2.Shandong Key Laboratory of Industrial Control Technology, Qingdao, Shandong 266071, China
    3.Qingdao Port International Co. Ltd., Qingdao, Shandong 266011, China
  • Online:2022-05-15 Published:2022-05-15

摘要: 合理配置与调度自动化集装箱码头岸桥、场桥和AGV(automated guided vehicle)等设备对提高码头作业效率,减少能耗具有重要意义。在集装箱码头缓冲区容量有限的条件下,结合AGV路径无冲突约束,建立了以最小化船舶在港时间和最小化总能耗为目标的多目标混合整数规划模型,并设计了双层遗传算法求解方法。以某市自动化集装箱码头为例,针对不同集装箱作业规模和决策目标进行仿真实验,对不同AGV路径冲突避免策略下的运行能耗进行比较。结果表明,以最小化船舶在港时间和最小化能耗为目标的AGV联合调度可在不发生路径冲突的前提下显著提高码头运行效率,降低能耗。

关键词: 自动化集装箱码头, 改进遗传算法, 多目标规划, 路径冲突, AGV调度

Abstract: Reasonably allocating and optimizing the quay crane, yard crane and AGV(automated guided vehicle) of automatic container terminal plays an important role in improving the operation efficiency and reducing the energy consumption. Under the condition of limited buffer capacity of container terminal, combined with the non-conflict constraint of AGV paths, a multi-objective mixed integer programming model is established to minimize vessel time in port and total energy consumption, and a double-layer genetic algorithm is designed to solve the problem. Taking a city port as an example, the simulation analysis is carried out for different container scales and decision objectives. The energy consumption of different AGV path collision avoidance strategies is compared. The results show that the proposed strategy can significantly improve the efficiency of terminal without path conflicts and reduce the energy consumption.

Key words: automated container terminal, improved genetic algorithm, multi-objective programming, path conflict, AGV dispatching