考虑岸桥缓存区和能耗节约的AGV协同调度研究  

Research on AGV cooperative scheduling considering quay crane cache area and energy saving

作　　者：陈信强 戴锦宇 刘恕浩 韩冰[4,5] CHEN Xinqiang;DAI Jinyu;LIU Shuhao;HAN Bing(Institute of Logistics Science&Engineering,Shanghai Maritime University,Shanghai 201306,China;Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China;Hubei Key Laboratory of Inland Shipping Technology,Wuhan University of Technology,Wuhan 430063,China;Shanghai Ship and Shipping Research Institute Co.Ltd.,Shanghai 200135,China;College of Physics and Electronic Information Engineering,Minjiang University,Fuzhou 350108,China)

机构地区：[1]上海海事大学物流科学与工程研究院,上海201306 [2]上海海事大学商船学院,上海201306 [3]武汉理工大学内河航运技术湖北省重点实验室,湖北武汉430063 [4]上海船舶运输科学研究所有限公司,上海200135 [5]闽江学院物理与电子信息工程学院,福建福州350108

出　　处：《中国航海》2024年第3期72-80,共9页Navigation of China

基　　金：国家重点研发计划(2021YFC2801002);国家自然科学基金(52102397,52472347);福建省自然科学基金(2022J01131710)。

摘　　要：针对自动化集装箱码头岸桥与自动导引小车(Automatic Guided Vehicle, AGV)之间相互等待以及由此产生的能耗浪费等问题,提出了考虑岸桥缓存区和能耗节约的AGV协同调度模型。以岸桥作业延误时间,岸桥和AGV双向等待时间以及设备等待延误能耗为目标建立了多目标混合整数规划模型。提出遗传-模拟退火算法,将遗传算法的结果作为模拟退火算法的初始解输入,在提升模拟退火算法初始解质量的同时还摆脱了遗传算法陷入局部最优的情况。用遗传-模拟退火算法对模型进行求解,并与其他算法进行对比,试验结果表明:遗传-模拟退火算法在收敛速度和收敛效果上相对于其他算法均有所提升。该研究对于码头决策者在调度作业中提高设备作业连续性、节约能耗,实现绿色低碳优化调度具有参考意义。Aiming at the problems of mutual waiting between quay crane and AGV(Automatic Guided Vehicle)in automated container terminals and the resulting waste of energy consumption,an AGV cooperative scheduling model considering quay crane cache area and energy saving is proposed.A multi-objective mixed integer programming model is established with the objectives of quay crane operation delay time,quay crane and AGV bidirectional waiting time and equipment waiting delay energy consumption.GA-SA(Genetic-Simulated Annealing Algorithm)is proposed,and the results of GA(Genetic Algorithm)are used as the input of the initial solution of SA(Simulated Annealing),which not only improves the quality of the initial solution of SA,but also gets rid of GA falling into the local optimum.The model is solved by GA-SA.Compared with other algorithms,the experimental results show that the convergence speed and convergence effect of GA-SA are better.This study has reference significance for terminal decision makers to improve the continuity of equipment operation,save energy consumption,and realize green and low-carbon optimal scheduling.

关 键 词：自动化集装箱码头 岸桥缓存区 能耗节约 AGV协同调度 

分 类 号：U691.3[交通运输工程—港口、海岸及近海工程]