采用蚁群算法求解铁路空车调整问题

Solving Railway Empty Cars Adjustment Problem by Ant Colony System Algorithm

蚁群算法是受自然界中蚁群搜索食物行为启发而提出的一种智能优化算法,针对空车产生总数和空车需求总数相等的平衡运输问题,建立以空车走行公里数最小为目标的空车调整数学模型,应用蚁群算法求解铁路空车调整问题。对有3个空车产生地点、4个空车需求地点的240辆空车平衡运输的算例,采用蚁群算法求解,得到2种目标结果最优的调整方案,可见该算法结果具有多重性,可以适应不同的调整需求。将其计算结果与分别采用最小元素法、西北角法、神经网络法及遗传算法所得结果进行比较,表明采用蚁群算法精度高、参数少、运算过程简单、模型易于理解和维护。采用蚁群算法求解空车调整模型可以用于全路、路局等的空车调整问题求解。

Ant Colony System （ACS） algorithm is a kind of intelligent optimization algorithm which has been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. Aiming at the problem of the balanceable transportation between the total quantity of empty cars occurred equal to the total quantity of empty cars required, the mathematical model of empty cars adjustment on railway network which aims at the minimization of consuming car-kilometer is established. The paper applies ACS to solve the problem of empty cars adjustment. A balanced transportation instance with 240 empty cars from 3 empty cars occurrence stations to 4 empty cars required stations was solved and obtained 2 optimized target results. It is obvious that this algorithm possesses multiplicity and is adaptive to different adjustment re- quirements. Calculation results got from ACS are compared respectively with the results obtained from minimum-element method, northwest-corner method, NN and GA. Results shows that using ACS to solve the problem not only saves developing time, but also has obvious advantage in using fewer arguments, with high precision simple computing, easy to understand and maintain. Therefore, ACS is an effective method to solve the problem of empty car adjustment and meets the need of dispatching in Railway Bureau.