Integrated Optimization Of Order Picking And Delivery Routing Of M Supermarket

With the rapid development of big data and artificial intelligence technology,the production,circulation,and sales process of goods are continuously upgraded and transformed,the traditional retail industry has gradually developed into a new retail model of “online-offline integration”.Under the new model environment,customers are increasingly pursuing a sense of shopping experience.At the same time,online orders have many customers,scattered distribution destinations,relatively small demand for goods in a single order,large differences in product categories,which will lead to the order picking and distribution difficulty,there is a high cost of order fulfillment,order fulfillment time is long even time-out and so on.Therefore,for enterprises,whether they can coordinate the selection and distribution of online retail projects through logistics management,at the same time,it is an urgent problem to pick up and package the goods from the express warehouse area and deliver them to the customers at low cost.The online retail project of M supermarket has been taken as the research object.Through research,it is found that its online retail project has problems such as the insufficient connection between the order picking and distribution difficulty,excessive picking time,low efficiency,and insufficient timeliness of distribution services.In response to these problems,under the premise that the order arrives online in real-time,the picking path is "S-shape",the number of picking personnel is limited,the order demand is unknown,and the fulfillment service time must be guaranteed,this article assumes that the total fulfillment cost from order picking to distribution is the smallest Turn it into a goal,comprehensively consider factors such as the fixed cost of distribution vehicles,variable distribution costs,and picking costs,and establish a joint optimization model of order picking and distribution paths with capacity constraints and loading rate constraints.The joint optimization of the order picking and distribution path is a typical NP-hard problem,and it is difficult to obtain accurate solutions with accurate algorithms for such large-scale problems.Therefore,the three-stage heuristic solution algorithm has been designed.The first stage is the preprocessing of the distribution area.According to the historical order data of M Supermarket,the number of divided areas is determined;At the same time,based on the delivery path and picking progress within the current order collection time,and under the premise of satisfying the constraints,the orders that have arrived within the next order collection time are inserted into the unsorted delivery path during the current order collection time using the nearest insertion method.Update picking batches to increase vehicle loading rate and reduce service costs.The second stage is the order distribution route optimization algorithm.Every ten minutes is a collection time.The K-means clustering algorithm is used to generate different clusters of orders within the collection time.The loading rate of each distribution route is considered.Under the premise,the re-clustering idea is added to the genetic algorithm to plan the distribution path in each cluster;The third stage is the optimization of order picking.It calculates the latest departure time of each distribution route and arranges it in chronological order to determine the order picking order of the distribution route.Then,the order is established based on the similarity clustering rule for the orders on each distribution route.Batch optimization model,and use seed algorithm to solve the batch order picking batch.Finally,the research scheme of order picking and path optimization is applied to the online retail project of M supermarket,and it is proved by comparing with the existing data of M supermarket,the joint optimization algorithm can not only improve the customer's satisfaction degree by completing the order distribution strictly within the promised time,but also improve the selection efficiency and distribution efficiency in the service cost,the service cost of the joint optimization is reduced by 18.3%,and the profit of the enterprise is increased,which proves the effectiveness and superiority of the algorithm.Finally,to provide better service to customers and improve the enthusiasm of employees,this paper adopts the method of combining analytic hierarchy process(AHP)with grey correlation degree to evaluate the service quality of the distribution personnel,thus for M supermarket to choose more high-quality staff to complete the distribution service.