数据驱动的自动化码头岸桥与AGV双层优化调度模型  

Two-layer Optimization Scheduling Model of Quay Cranes and Automated Guided Vehicles with Data-driven Methods at Automated Container Terminals

作　　者：李兴春 李明泽 曾庆成 杨昂 LI Xingchun;LI Mingze;ZENG Qingcheng;YANG Ang(School of Maritime Economics and Management,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连海事大学航运经济与管理学院,辽宁大连116026

出　　处：《工程管理科技前沿》2024年第6期25-32,共8页Frontiers of Science and Technology of Engineering Management

基　　金：国家自然科学基金资助项目(72203029);教育部人文社会科学研究基金资助项目(22YJCZH210)。

摘　　要：自动化集装箱码头中岸桥和自动引导车(automated guided vehicle,AGV)紧密衔接,需要统筹规划以追求整体最优。为了提高岸桥和AGV作业系统的协同性,提出了一个双层优化模型,同时决策岸桥和AGV的配置与作业顺序。在AGV自动路径寻优模式下,为控制其行驶时间的不确定,本文基于码头历史作业数据,采用分布鲁棒优化方法刻画AGV行驶时间,建立模糊机会约束控制岸桥等待风险,构建数据驱动的双层优化模型并得到等价形式。为求解该模型,基于L-shaped算法框架,设计一种结合模拟退火算法和邻域搜索算法的自适应大邻域搜索算法求解。实验结果表明:本文模型算法能够有效提高岸桥和AGV调度的协同性,成功解决实际作业规模案例,提升整体运输体系效能。The quay cranes and Automated Guided Vehicles(AGVs)in an automated container terminal are closely interconnected.This necessitates comprehensive planning for overall optimization.A two-layer optimization model is proposed to enhance coordination between quay cranes and AGV operational systems,concurrently determining the configuration and operational sequence of both quay cranes and AGVs.Within the context of AGVs’automatic path optimization mode,this study aims to manage the uncertainty in AGV travel times.Leveraging historical operational data from the terminal,a distributionally robust optimization method is employed to characterize AGV travel times.Additionally,a fuzzy chance constraint is established to mitigate the risk of quay crane waiting,resulting in the construction of a data-driven two-layer optimization model and its equivalent formulation.To solve this model,an adaptive large neighborhood search algorithm,combining simulated annealing and neighborhood search algorithms,is designed within the framework of the L-shaped algorithm.Experimental findings demonstrate that the proposed model and algorithm effectively enhance the coordination of quay cranes and AGVs,successfully addressing real-world operational scale cases,thereby improving the overall efficiency of the transportation system.

关 键 词：自动化集装箱码头 数据驱动 L-shaped算法 分布鲁棒优化 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置] U691.3[自动化与计算机技术—控制科学与工程]