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针对铁路集装箱堆场混堆区中的零散箱箱位分配问题,在集装箱到达时间和离开时间已知的条件下,建立一个计划期内以倒箱次数最少为目标的多时段动态集装箱堆场箱位分配模型。在计划期内将每时段起重机作业按顺序划分为提箱和卸车两个过程,采用5组0-1变量刻画两种作业、作业前后集装箱的状态以及箱位的状态,由此建立非线性0-1规划模型。考虑到模型所刻画的问题具有NP-hard性质,设计遗传算法求解。算例表明:该模型及算法能够有效地优化堆场内零散箱的箱位分配,提高集装箱堆场的作业效率。
Aiming at the distribution problem of scattered boxes in the mixed heap area of railway container yard, a multi-period dynamic container yard box with the least number of broken boxes as the goal during the planned period is established under the condition that the arrival time and departure time of the container are known. Bit allocation model. During the planning period, the crane operations at each time period are divided into two processes: suitcase loading and unloading in sequence. Five groups of 0-1 variables are used to characterize the two jobs, the status of the container before and after the job, and the status of the box. Thus, a nonlinear 0- 1 planning model. Considering the NP-hard nature of the problem portrayed by the model, a genetic algorithm is designed to solve it. The example shows that the model and the algorithm can effectively optimize the distribution of boxes in the scattered boxes in the yard and improve the working efficiency of the container yard.