摘要
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 can be solved through the genetic algorithm by being equivalent to a TSP problem. Problem 1： Set up combinatorial optimization model grading by taking the disintegrated order of the train arrival field as decision variable, the vehicle ＂mean-time＂ as minimum goal ; Problem 2 ： Give priority to disintegrating the trains of which are military ones and have the destination of S1 based on Problem 1, which can be got decompile programs; Problem 3： Delay 2 hours for the scope extension of the vehicles to be de-compile. Problem 4： Change the destination of the trains from S1 and S2 to between E3 and the south of E4. Problem 5： Because of the trains which are done by organizing the groups being timely--dispatched ,pick up the within--separate decompile strategy which can be used for the currently-arriving vehicles after arranging the left ones during the previous time. Problem 6： Give suggestions on improving the marshalling plan.
出处
《数学的实践与认识》
CSCD
北大核心
2009年第16期154-161,共8页
Mathematics in Practice and Theory
关键词
组合优化问题
旅行商问题
遗传算法
中时
combinatorial optimization model
TSP problem
genetic algorithm
mean-time
