论文部分内容阅读
主要通过建立组合优化的模型,将原问题等价为一个TSP问题,运用遗传算法来求解.问题一:以到达场列车解体次序为决策变量,车辆“中时”最小为目标,分阶段建立组合优化模型;问题二:在问题一的基础上将含有军用车辆的列车和含有去向目的站点S1车辆的列车优先考虑解体,得到解编方案;问题三,将待解编列车的范围向后延伸2小时;问题四,将到达场列车中去向目的站点S1和S2以远的车辆分别排在目的站点E 3和E 4以南之间;问题五,由于编组完成的列车都能及时发出,当排完前一时段留下的车辆后,对于当前时段到达的列车采用随到随解策略进行解编;问题六,给出改进编组调度方案的建议和意见.
Mainly through the establishment of combinatorial optimization model, the original problem is equivalent to a TSP problem, the use of genetic algorithm to solve the problem one: the arrival of the train disintegration order as a decision variable, the vehicle “in time ” as the minimum target, phased To establish a combined optimization model; Question 2: On the basis of Question 1, the train containing the military vehicles and the train containing the S1 vehicles destined for the destination site will be given priority to be disassembled to obtain a solution program; and Problem 3: Extension of 2 hours; Fourth, the arrival of the train to the destination site S1 and S2 vehicles are far behind the destination site E 3 and E 4 south; Question five, as the formation of the train can be issued in time, When the vehicles left behind in the previous period are exhausted, the train arriving at the current time is solved by using the random strategy. Question 6: Suggestions and suggestions for improving the scheduling of marshalling are given.