关键词: 众包, 高德地图API, 改进蚁群算法, 路径优化

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 interface, the longitude and latitude information of each actual node are obtained, then, distances between each node can be calculated. 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 algorithm, the improved ant colony algorithm has obvious advantages in solving the quality and efficiency.

Key words: crowdsourcing, Amap API, improved ant colony algorithm, routing optimization