混堆模式下基于动态规则NSGAⅡ的自动堆垛起重机作业优化

Optimization of automated stacking crane operation based on NSGA Ⅱ with dynamic rules in mixed stacking mode

针对外集卡到达时间的不确定性,提出自动堆垛起重机(ASC)作业序列的动态优化,从而以减少ASC作业完成时间以及ASC和外集卡等待时间为目的,提高自动化集装箱码头堆场的作业效率。首先,结合混堆模式下集装箱作业类型与外集卡动态到达的特点,提出ASC动态匹配外集卡作业任务的策略;其次,构建ASC作业时间最短与ASC和外集卡等待时间最短的多目标模型;最后,设计基于动态规则的非支配排序遗传算法Ⅱ(DRNSGAⅡ)作为求解算法。在小规模算例实验中,分别运用DRNSGAⅡ与遗传算法(GA)求解动态策略和随机策略下的ASC作业问题。实验结果表明,DRNSGAⅡ求解的动态策略下目标函数值优于随机策略28.2%,并且动态策略下DRNSGAⅡ的求解结果优于遗传算法23.3%。在大规模算例实验中,比较了DRNSGAⅡ与多目标粒子群优化(MOPSO)两种算法的性能。实验结果表明DRNSGAⅡ的求解结果优于MOPSO算法6.7%。可见DRNSGAⅡ能够快速生成多样化的非支配解,为混堆模式下的ASC动态作业提供决策支持。

Aiming at the uncertain arrival time of external container trucks, the dynamic operation sequence of Automated Stacking Crane(ASC) was optimized to improve the operation efficiency of automated container terminal yard with objective of reducing the completion time of ASCs as well as the waiting time of ASCs and external container trucks.Firstly, combining the characteristics of container operation types and dynamic arrival of external container trucks in mixed stacking mode, a strategy of ASCs dynamically matching the operation tasks of external container trucks were proposed.Then, a multi-objective model with the shortest operating time of ASCs as well as the shortest waiting time of ASCs and external container trucks was constructed. Finally, a Non-dominated Sorting Genetic Algorithm Ⅱ based on Dynamic Rules(DRNSGA Ⅱ) was designed as the solving algorithm. In small-scale example experiments, DRNSGA Ⅱ and Genetic Algorithm(GA) were used to solve ASC operation problems under dynamic strategy and random strategy, respectively. The experimental results show that the target function value solved by DRNSGA Ⅱ under dynamic strategy is 28. 2% better than that under random strategy, and the result solved by DRNSGA Ⅱ is 23. 3% better than that solved by Genetic Algorithm(GA) when using dynamic strategy. The performance of DRNSGA Ⅱ and Multi-Objective Particle Swarm Optimization(MOPSO) algorithm were compared in large-scale experiments. The experimental results show that the result solved by DRNSGA Ⅱ is 6. 7% better than that solved by MOPSO algorithm. It can be seen that DRNSGA Ⅱ can quickly generate a variety of non-dominated solutions to provide decision support for ASC dynamic operation in mixed stacking mode.