蚁群算法物流配送中心选址优化仿真研究

摘要

In order to effectively save vehicle transportation costs, the urban vehicle scheduling should be optimized, and the paper proposed an logistics distribution site selection method based on an improved ant colony optimization algorithm. The algorithm first broke down the values of the solution into solution pairs, and then used the improved ant colony optimization algorithm to turn the solution paies from uncertainty probem into certainty one, which greatly reduced the solving process. The simulation results show that the optimization scheduling algorithm reduces the computational complexity, optimizes the location model, and provides new a way for the location of logistics distribution.%研究物流配送选址优化调度问题.为了有效节约车辆运输成本,应选择最优路径.城市车辆调度路径选择,存在路网复杂性,参数设置较多,传统的调度算法存在计算复杂度高,不利于实际应用.为解决优化选址问题,提出了一种改进的蚁群优化物流配送选址方法.算法把求得的解首先分解为解对,然后通过改进的蚁群优化算法将解对从不确定性转变成确定性问题,可以大大的降低求解过程.通过仿真表明,提出的优化算法不但降低了计算的复杂度,优化了选址模型,而且为解决物流选址问题提供了新的有效途径.