多车场多车型车辆调度问题的改进粒子群算法

多车场多车型车辆调度问题优化是物流配送中的典型NP难解问题，针对传统的粒子群算法存在收敛速度慢，易早熟收敛等问题，提出了一种改进的粒子群优化算法。该算法对种群中的粒子采用一定的概率进行柯西变异，使算法跳出局部最优解。将算法应用于多车场多车型车辆调度问题优化，算例证明该算法求解多车场多车型车辆调度问题是可行的，并且优于标准粒子群优化算法。

Study on multi-depots and multi-vehicles vehicle scheduling problem based on improved particle swarm optimization

Multi-Depots and Multi-Vehicles Vehicle Scheduling Problem（MDMVVSP）is a kind of NP combination problem in logistics distribution. In order to overcome the problems such as long computing time and easy to fall into local best for traditional Particle Swarm Optimization（PSO）, an Improved PSO（IPSO）algorithm is put forward. In the algorithm, Cauchy mutations are used to ensure lager mutation steps and escape from local optimal solution. The algorithm is applied to MDMVVSP, the simulation results show that the algorithm is feasible to solve the MDMVVSP, and it is better than PSO.