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

摘要： 针对现有O2O外卖众包配送的经验依赖性和随机性问题，建立以距离成本和时间惩罚成本之和最小化为目标的带有单侧软时间窗的需求可延迟的开放式车辆路径优化模型，并借助高德地图API接口获得各实际节点的经纬度信息和各节点间距离。改进蚁群算法在状态转移规则中添加下一步移动的潜在客户数量影响因子，同时将确定性搜索与随机性搜索结合，缩小蚂蚁搜索范围。仿真实验结果表明，相较于标准蚁群算法和标准粒子群算法，改进蚁群算法在求解质量和效率上均具有明显的优势。

关键词: 众包, 高德地图API, 改进蚁群算法, 路径优化