基于粒子群算法的多目标车辆调度模型求解

车辆调度问题是具有复杂约束条件的组合优化问题,在理论上属NP-hard问题.考虑车辆数目最少和车辆运行时间最短,建立了具有时间约束的多目标车辆调度模型.并采用粒子群算法（PSO）求解车辆调度问题,以寻求最优车辆调度方案.在实例中通过运用粒子群算法和遗传算法进行比较分析,结果表明,PSO算法简单可行,在优化性能、收敛速度及鲁棒性等方面优于遗传算法,能较好地解决组合优化问题.

Solution of Multi-Objective Vehicle Scheduling Model Based on Particle Swarm Optimization

Vehicle scheduling is the combinational optimization problem that has complex restricted conditions.It belongs to the NP-hard problem theoretically. A multi-objective vehicle scheduling model based on the time constraints was established by considering the minimal vehicle number and the shortest vehicle runtime.Based on this model,a particle swarm optimization(PSO) based scheduling algorithm was proposed to obtain the optimum vehicle scheduling scheme.A scheduling example was resolved by the PSO and generic algorithm(GA).The result shows that the proposed algorithm is simple and feasible,with better performances in optimization,convergence speed and robustness compared with GA.