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基于遗传算法的集散型外卖配送服务网络设计研究
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Abstract:
外卖配送时间窗短，目前采用零工经济模式，由配送员直接到商家取货并送至客户处。随着外卖市场规模扩大，或遇突发事件等订单激增的情况，会出现运力不足，等待时间大幅延长。研究了集散型网络在外卖配送中的应用，划分商家和客户群体，建立路径优化模型，求解集散型配送网络的最优路径并计算其成本。与零工经济模式下的配送网络进行对比，结果表明集散型网络在订单达到一定规模时更有效，可以使用较少配送员完成配送任务，同时具有明显的成本优势。当订单小于一定规模时，两种网络对配送员数量的需求相似，但集散型网络依然存在成本优势。
Takeout delivery has a short delivery window and currently adopts the gig economy model, in which delivery workers pick up goods directly from merchants and deliver them to customers. As the food delivery market grows in size, or in the event of a surge in orders such as emergencies, there is a shortage of capacity and waiting times lengthen significantly. This paper studies the application of distributed network in takeout delivery, divides merchant and customer groups, establishes a path optimization model, solves the optimal path of distributed distribution network and calculates its cost. Compared with the distribution network under the gig economy model, the results show that the distributed network is more effective when the order reaches a certain scale, which can use fewer delivery workers to complete the distribution task, and has obvious cost advantages. When the order is smaller than a certain size, the two networks have similar demands for the number of deliverers, but the distributed network still has cost advantages.
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