基于遗传算法的不同约束条件车辆调度问题研究

Study on VSP with different constraints based on genetic algorithm

研究了不同约束条件下,车辆调度问题的最短行车距离。分别针对带有里程约束限制、带有时间窗口限制、带有满载和非满载约束限制的约束条件,建立了不同的车辆调度模型。针对车辆调度模型设计了自然数编码的遗传算法,并改进了遗传算法中的交叉算法,尽可能地保留了最优基因的遗传性,加速了算法的收敛性。最后进行算例分析,结果表明了模型和算法的有效性。

This paper studied the shortest run distances of vehicle scheduling problem （ VSP） with different constraints. Established the vehicle scheduling models with different constraints include driving distance,time window,full loads and non-full loads. And then designed genetic algorithm of natural number coding for vehicle scheduling models and improved the cross algorithm of it. Retained the optimal gene transmissibility as much as possible and accelerated the convergence of the algorithm. Finally,verified the model and algorithm through numerical example.