【摘要】 在涉及物资分发与收集或提供服务的诸多部门中有着各种各样的 VRP(车辆路由问题 ) ,现有的对于 VRP的研究主要集中在需求是确定性的情形。由于实际情况中需求往往是随机的 ,随机性需求情形 VRP的研究近年来得到了国内外学者的重视。本文利用 Hopfield人工神经网络解组合最优化问题时计算量不随维数指数增加这一优点 ,针对一类随机需求情形 VRP给出了一种 Hopfield人工神经网络解法。文中描述了相应于该 VRP优化问题的优化变量的编码 ,能量函数的构造和网络方程的推导 ,并通过算例考察了该算法的寻优性能。结果表明 ,该算法具有较好的局部寻优和整体寻优性能。更多还原

【Abstract】 Various vehicle routing problems (VRPs) are encountered in many service systems such as delivery,customers pick up, repair and maintenance services. Most of existing VRP researches have been concentrated in the case of deterministic demands. Because of practical needs, the researches on vehicle routing problem with stochastic demands begin to draw much attention at home and abroad. This paper proposes an algorithm based on Hopfield neural network to solve the VRP with some kind of stochastic demands. The specific energy function and the network equations corresponding to the stochastic VRP are derived. The behavior of the algorithm is tested with numerical examples. The results show that the algorithm has good performance in both global and local searching. It is believed that the concurrent and adaptive mechanisms of neural network make the algorithm so efficient.更多还原