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集装箱翻箱问题(CRP)可描述为在集装箱堆场现有堆垛状态和提箱序列确定情况下,以最少的翻箱数量提取出堆场箱区内所有集装箱;CRP是一个NP-hard问题。为此构建双层目标规划模型,提出嵌套翻箱规则的路径规划算法(POA),以期减少解空间大小,从而在更短的CPU运行时间内得到CRP的最优解。数值实验结果表明,POA在翻箱数量及运行时间上均优于多数算法,有效提高集装箱码头堆场作业效率,更适用于求解集装箱码头翻箱作业优化问题。
The CRP can be described as extracting all containers in the yard area with a minimum number of containers, with the existing stacking conditions of the container yard and the suitcase sequence identified; CRP is an NP-hard issue. For this purpose, a two-level objective programming model is constructed and a nested rule-based path planning algorithm (POA) is proposed in order to reduce the size of solution space and obtain the optimal solution of CRP in a shorter CPU runtime. The numerical results show that POA is superior to most algorithms in terms of the number and duration of rollover cases, which can effectively improve the operation efficiency of container terminal yard and is more suitable for solving the problem of roll-over of container terminal.