计算机工程与应用2019，Vol.55Issue(8)：244-249,6.DOI:10.3778/j.issn.1002-8331.1808-0453
基于改进蚁群算法的众包配送路径研究
Research on Crowdsourcing Distribution Path Based on Improved Ant Colony Algorithm
摘要
Abstract
In view of empirical dependence and randomness problems of the crowdsourcing distribution of the existing O2O takeout business, a demand-delayed open vehicle routing optimization model with a single side soft time window is established. This model is aimed at minimizing the cost of distance and time penalty. With the help of the Amap API inter-face, the longitude and latitude information of each actual node are obtained, then, distances between each node can be cal-culated. The improved ant colony algorithm adds the potential customer number impact factor for the next move in the state transition rule, and combines deterministic search with random search to reduce the ant search range. The simulation results show that compared with the standard ant colony algorithm and the standard particle swarm optimization algo-rithm, the improved ant colony algorithm has obvious advantages in solving the quality and efficiency.关键词
众包/高德地图API/改进蚁群算法/路径优化Key words
crowdsourcing/ Amap API/ improved ant colony algorithm/ routing optimization分类
管理科学引用本文复制引用
蒋丽,王静,梁昌勇,赵树平..基于改进蚁群算法的众包配送路径研究[J].计算机工程与应用,2019,55(8):244-249,6.基金项目
国家自然科学基金(No.61773297,No.61702381,No.61602351). （No.61773297,No.61702381,No.61602351）
