Abstract: The epidemic has accelerated the expansion of traditional retailers to online businesses and the emergence of new retail models integrating online and offline, making the timeliness of retail logistics and service quality the key to winning the market in the post-epidemic era. Under the new retail model, the logistics demands of consumers are more dispersed and the batch frequency is higher. Different orders and logistics distribution need to be uniformly deployed, which puts forward higher requirements for the service quality and timeliness of logistics distribution. Retail logistics distribution service requires the cooperation among logistics personnel, distribution vehicles and customers, ignoring the differentiated service time of logistics personnel, the different departing time of vehicles due to different release dates, and the timeliness of customer demand lead to high cost, poor timeliness and low customer satisfaction of retail logistics distribution schemes. When logistics personnel are familiar with the surrounding environment of customers, they can quickly and accurately find the distribution address and appropriate parking points, and even get familiar with the traffic conditions to avoid the congested sections. Especially in the post-epidemic era, the epidemic situation presents a multi-point distribution state, customer demands are scattered and change every day, and logistics distribution personnel are also absent due to temporary lockdown. If differentiated service time is ignored, this will not be conducive to the full utilization of human resources, or to the response to emergencies. Order release dates determines the vehicle departure and distribution time, which not only affects the collaborative decision of order assignment and route planning, but also has an important impact on distribution efficiency. In addition, as the distribution process is often accompanied by force majeure factors such as bad weather and traffic jams, the customer delivery time window is usually flexible, which means that the customer is allowed to receive the service earlier or later than the specified time window to a certain extent, but the violation of the specified time window will lead to the decline of customer satisfaction, so the corresponding punishment is considered in the objective function.
   In this context, we study the collaborative scheduling problem of retail logistics aiming to minimize travel cost, penalty cost and differentiated service time cost. Under the constraints of order release dates and customer flexible time window, dispatching a group of logistics distribution personnel to complete the delivery tasks of customer orders is a collaborative optimization of task assignment and vehicle routing planning for multiple logistics distribution personnel with differentiated service time.To solve this problem, a linear mathematicalprogramming model is established with the optimization objective,and the improved iterated local search algorithm based on large neighborhood search process is designed in this paper. This algorithm uses regret repair operator to generate high quality initial solution to enhance the search efficiency, and introduces large neighborhood search with four removal operators and two repair operators, a breaking mechanism and optimal service start time model to enhance the global optimal search ability of the algorithm.
   Finally, the numerical experiment verifies the effectiveness of the model and algorithm by solving benchmarking instances and the numerical instances in the paper, and the sensitivity analysis of the parameters gives corresponding management enlightenment, which provides effective reference and suggestions for the effective management of distribution personnel, efficiency improvement of distribution and cost control of retail logistics operation management in the post-epidemic era.

Key words: retail logistics, differentiated service time, release dates, collaborative optimization

摘要： 疫情加速了传统零售商拓展线上业务,使后疫情时代零售物流配送的及时性和服务质量成为制胜的关键点。零售物流配送需要物流配送人员、配送车辆和客户协作完成,忽略物流配送人员的服务时间差异化、订单释放时间不同导致的车辆出发配送时间不同以及客户需求的时效特点等将导致零售物流配送方案成本高、时效差且客户满意度低。针对此问题,本文以物流配送成本、惩罚成本和服务时间成本之和最小为目标建立了数学模型,设计了改进的基于大邻域搜索的迭代局部搜索算法。该算法采用后悔修复算子生成高质量的初始解以增强搜索效率,引入带有定制化的四种移除算子和两种修复算子的大邻域搜索、打破机制和求解最优服务开始时间的数学模型以增强算法的全局寻优能力。最后,数值实验部分通过求解标杆算例和生成算例验证了模型和算法的有效性,并对参数进行灵敏度分析,结果可为后疫情时代零售物流运营管理的配送人员有效调度、配送效率提升和成本控制提供有效参考。

关键词: 零售物流, 差异化服务时间, 订单释放时间, 协同调度优化