基于蚁群算法的天然气车辆调度优化

Optimization of Natural Gas Vehicle Scheduling Based on Ant Colony Algorithm

根据液化天然气(LNG)运输车辆调度中车辆载重量受限和卸气等待时间过长的特殊性,建立一个每次只配送两个子站的LNG车辆调度模型,同时进一步改进蚁群算法,将蚁群算法与抽样方法相结合,使得算法具有较快的收敛速度,并通过试验证明改进算法的有效性和可行性。将蚁群算法与LNG车辆调度相结合,进一步优化蚁群算法,用两两抽样的方法对某单配送中心LNG企业的车辆调度进行优化,寻找最优路径,达到最小成本,提高蚁群算法的遍历速度,减少整个搜索进程耗时。从理论上找到一个LNG车辆调度蚁群算法优化的方案,降低等待时间,优化运输路线,提高公司利润。

According to the Liquefied natural gas（LNG）transportation vehicle scheduling of vehicle load and unload gas limited waiting time is too long,a special time distribution sub station model for two LNG vehicle scheduling is established,and an improved ant colony algorithm,is further established which combines ant colony algorithm with sampling method.The algorithm has a faster convergence rate,and proved the validity and feasibility by test.The ant colony algorithm is combined with LNG vehicle scheduling,the ant colony algorithm is further optimized.The vehicle scheduling of a single distribution center of LNG company is optimized by two-by-two sampling,to find the optimal path and minimum cost,improve the traversal speed of ant colony algorithm,and reduce the time-consuming search process.A scheme to optimize the LNG vehicle scheduling ant colony algorithm is found theoretically,which reduces the waiting time,optimizes the transportation route,and improves the company’s profit.