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由于编组站解、编作业时间存在一定的波动性,将其作为模糊变量,用变量的λ悲观值表示在一定置信水平下的解、编作业时间,以阶段内出发车辆数最大为目标,建立不确定条件下的编组站动态配流模型。通过定义不确定条件下的可解集合、待解集合和选解集合将动态配流问题映射为方案树,从而设计一种基于蚂蚁系统的非确定性树搜索算法。由于改进了蚂蚁系统的选择策略和信息素更新,并在每次转移过程中对模型的约束条件进行判断,提高了解的性能和算法的收敛速度。算例表明,该算法能够较快地搜索到有利的全局方案。
Because of the marshalling station solution, there is a certain volatility in the editing time, which is regarded as the fuzzy variable. The λ pessimistic value of the variable is used to express the solution under a certain confidence level. The working time is set as the goal, Dynamic Assignment Model of Marshalling Station under Uncertainty. By defining the solvable set, the solution set and the solution set under uncertainty, the dynamic allocation problem is mapped into a scheme tree, so as to design a nondeterministic tree search algorithm based on ant system. Due to the improvement of ant system selection strategy and pheromone updating, the constraints of the model are judged during each transfer process to improve the performance and convergence rate of the algorithm. The example shows that this algorithm can search favorable global solution quickly.