不确定环境下进口箱提箱作业动态优化模型与算法

基于提箱作业的动态决策特点，构建以分组提箱为核心的进口箱提箱动态优化数学模型；基于统计分布规律模拟外部集卡抵港的随机性，提出动态优先提箱概率的概念及计算方法，并以此为基础设计分支定界+启发式算法的双层算法对模型进行求解.通过一系列大规模实验并与诸如IH、OH、RDH、Max-Min等现有算法的比较显示了该动态优化算法在不确定环境下对进口箱提箱作业调度优化的有效性与鲁棒性.
Based on the characteristics of dynamic decision making for retrieving operations，a mathematical model considering batch arrival was developed to optimize the retrieving operations of import containers. Based on statistical distributions, the randomness of external trucks arriving was simulated. The concept and calculation method of dynamic probability for prior retrieving were proposed to design the double algorithm including Branch & Bound and heuristics, and to solve the model. A series experiments show the superiority and robustness of the proposed dynamic optimization algorithms for retrieving optimization of import containers under uncertainty environment as compared with existing algorithms such as IH, OH, RDH, MaxMin, etc.