Abstract: New retail has led to the transformation of traditional enterprises, resulting in the emergence and continuous development of order fulfilment models in which physical stores act as front warehouses. In response to order demand uncertainty and store inventory changes, the problem of joint optimization of order split and delivery under multi-store collaboration is proposed for situations arising in order fulfilment at the nearest store. By introducing a limit on the number of split orders, the problem-solving space is reduced. To reduce the path overlap caused by separate deliveries, a collaborative delivery model is used to integrate the paths. The order fulfilment cost is reduced by optimizing the adjustment between order split and delivery. Integrating breadth-first search and local search algorithms, the TNILS hybrid heuristic algorithm is constructed to solve the problem. Based on the synthetic dataset, the effectiveness of collaborative delivery and the feasibility of the proposed algorithm are demonstrated by comparing the results of collaborative delivery with those of separate delivery. Finally, the effectiveness and stability of the TNILS（top-N & improved local search）algorithm are verified by comparing the experimental results with other algorithms.

Key words: route integration, order fulfillment, co-delivery, joint optimization, top-N &, improved local search（TNILS） algorithm

摘要： 新零售带动传统企业转型，加速了以实体门店作为前置仓的线上订单履行模式的发展。针对订单需求不确定导致的就近门店无法满足订单需求的情况，提出多门店协同下的订单拆分与配送的联合优化问题。通过引入拆单数量限制，缩减问题求解空间，同时为了减少单独配送导致的路径重叠，采用协同配送的模式整合路径，并通过订单拆分与配送之间的调整优化降低订单履行成本。集成广度优先搜索和局部搜索算法，构造TNILS（top-N & improved local search）混合启发式算法求解问题。在合成数据集的基础上，通过协同配送与单独配送的结果对比，证明了协同配送的有效性及提出算法的可行性。通过与其他算法的实验结果对比，验证TNILS算法的有效性和稳定性。

关键词: 路径整合, 订单履行, 协同配送, 联合优化, TNILS算法