基于状态节点网络的龙门吊路径优化问题研究

Research on Container Yard Crane Routing Problem Based on State-Node Network Optimization

为提升集装箱堆场的作业效率和服务水平,研究了集装箱堆场龙门吊的作业路径问题。将龙门吊的作业任务划分为阶段任务和阶段内子任务后,通过定义状态、状态值以及状态间距,提出了基于状态节点网络的龙门吊路径优化模型。并针对模型的特点,设计求解算法,算法由递归算法生成状态节点空间,用整数编码的遗传算法进行状态组合寻优。最后,以某集装箱码头实际作业数据为基础,进行了模型和算法的验证计算,结果显示基于状态节点网络的路径优化方法有效地减少了龙门吊的作业移动距离。

In order to improve the operation efficiency and service level of container yard, the yard crane routingproblem is studied. The tasks of yard crane are classified as phase tasks and container group sub tasks. Then, astate -node network optimization model is presented for yard crane routing problem, under the definition of thestate, state value and state distance. And further, a solving algorithm including two stages is designed for themodel. The first stage is a recursive algorithm for generating the state-node space, and the second stage is aninteger coded genetic algorithm for finding the optimal solution. Finally, the model and algorithm are tested to bevalid by a data case from a real container terminal, which shows that they can effectively reduce the movedistance of yard crane.