从外卖配送员角度出发提出一种改进蚁群算法(Improved Ant Colony Optimi-zation,IACO),在此基础上进行外卖配送路径规划研究.首先通过蚁群算法(Ant Colony Optimization,ACO)求解得到初始规划路径,然后通过大规模邻域搜索算法(Large Neighborhood Search,LNS)优化初始规划路径,通过将ACO和LNS算法结合,提高求解质量.为了验证方法的有效性,对外卖配送过程进行仿真,并且选用不同订单数量场景进行对照分析.根据最优配送方案路线图和目标罚函数的最优值可以得出,IACO 算法是有效的,且可以提高外卖配送员外卖配送的效率.IACO算法不但能够提升配送的智能化水平,还从外卖配送员的角度提出一种更为人性化的配送方法,支持网络互联外卖平台派送系统的可持续化发展.
Takeout delivery path planning based on improved ant colony optimization algorithm
It is impossible for takeaway delivery staff to plan the takeout delivery route balancing rationality and ef-ficiency.To address this problem,an Improved Ant Colony Optimization(IACO)algorithm is proposed.The initial routes are obtained using the Ant Colony Optimization(ACO)algorithm and then optimized using Large Neighbor-hood Search(LNS)algorithm.The solution quality is improved by combining the ACO algorithm with the LNS algo-rithm.The proposed algorithm is verified by simulating the delivery routes for different number of takeout orders.Comparative analysis shows that the proposed IACO algorithm can increase the takeout delivery efficiency,according to the optimal distribution plan and the ideal value of the objective penalty function.The proposed strategy can en-hance the intelligence and promote the long-term growth of the delivery system of Internet-connected takeout plat-forms.
improved ant colony optimization(IACO)large neighborhood search(LNS)takeout deliverydistri-bution schemes
万方数据
维普
