Optimizing Delivery Paths in Community Group Purchasing Using K-Means Clustering and Simulated Annealing Algorithm


In this paper, a path optimization algorithm is proposed to reduce the cost of delivery in community group purchasing. The algorithm consists of two main steps: first, the K-Means Clustering algorithm is used to screen the delivery points, and then the Simulated Annealing Algorithm is used to calculate the delivery path. In a community group purchasing scenario, there are m customer points, each of which is assigned to a delivery point. By applying the proposed algorithm, some of the n delivery points can be eliminated, reducing the delivery route and optimizing the average distance between the customer points and the delivery points.
The proposed algorithm takes into account various factors that affect the efficiency of delivery systems, such as delivery time, distance, and cost. By combining K-Means Clustering and Simulated Annealing Algorithm, the proposed approach offers a robust and flexible solution for optimizing delivery paths in community group purchasing. The results of our experiments indicate that the proposed algorithm can effectively reduce delivery time and costs compared to traditional delivery methods. These findings demonstrate the practical value of the proposed approach for companies involved in community group purchasing and delivery services. By providing an efficient and cost-effective delivery system, the proposed algorithm has the potential to improve customer satisfaction and increase business efficiency. This study has important implications for the future development of delivery systems in community group purchasing and similar applications.