基于改进NSGA-Ⅱ的考虑自动引导车充电策略的集成调度

Integrated scheduling considering automated guided vehicle charging strategy based on improved NSGA-Ⅱ

针对自动引导车(AGV)在自动化集装箱码头(ACT)执行任务过程中的电量问题,提出基于改进的非支配排序遗传算法-Ⅱ(NSGA-Ⅱ)的考虑AGV充电策略的集成调度。首先,在岸桥、场桥和AGV集成调度模式下,考虑AGV在不同作业状态下的耗电量,并建立以最小化作业完工时间和总耗电量为目标的多目标混合规划模型;其次,为提高传统NSGA-Ⅱ的性能,设计自适应NSGA-Ⅱ,并将所提算法与CPLEX求解器、NSGA-Ⅱ和多目标粒子群优化(MOPSO)算法进行性能对比;最后,设计AGV不同充电策略并对设备数量配比进行实验研究。算法对比实验结果表明:相较于传统NSGA-Ⅱ算法,自适应NSGA-Ⅱ对双目标的优化分别提升了2.8%和2.63%。利用自适应NSGA-Ⅱ进行的充电策略和设备数量配比实验的结果表明:增加AGV充电次数能够减少AGV的充电时间,且调整设备数量配比至3∶3∶9和3∶7∶3时,场桥和AGV的时间利用率分别达到最高。可见,AGV充电策略及设备数量配比对码头多设备集成调度有一定影响。

Aiming at the power problem of Automated Guided Vehicle(AGV)in the process of performing tasks in Automated Container Terminal(ACT),an integrated scheduling considering AGV charging strategy based on improved Nondominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ)was proposed.Firstly,considering the power consumption of AGV under different operating statuses in the integrated scheduling mode of quay crane,yard crane and AGV,a multi-objective mixed programming model with the goal of minimizing the completion time and total power consumption was established.Secondly,to improve the performance of the traditional NSGA-Ⅱ,an adaptive NSGA-Ⅱwas designed and compared with CPLEX solver and Multi-Objective Partical Swarm Optimization(MOPSO)algorithm on performance.Finally,different charging strategies and equipment number ratios of AGV were designed for experimental research.The experimental results of algorithm comparison show that the solution results of the adaptive NSGA-Ⅱare improved by 2.80%and 2.63%respectively on the two objectives proposed compared with NSGA-Ⅱ.The experimental results of applying the adaptive NSGA-Ⅱto study the ratio of charging strategies and equipment number ratios show that increasing AGV charging number can reduce AGV charging time,and adjusting the ratio of the equipment number to 3:3:9 and 3:7:3 lead to the highest time utilization of yard crane and AGV respectively.It can be seen that the AGV charging strategy and equipment number ratio can influence the terminal integrated scheduling with multiple equipment.