Design of Supply Chain Logistics Intelligent Management Information System Based on GIS Optimization Model.

With the improvement of productivity and the development of internet and information technology, many e-commerce systems and supply chain logistics systems have emerged, allowing for online purchases of food such as vegetables and fruits, as well as clothing and shoes. Nowadays, online shopping platforms such as Duoduo Maicai, Taobao, JD, and Meituan Youxuan have been integrated into people's lives, and various supermarkets and shopping malls are also everywhere. Therefore, the safety, efficiency, and real-time location of goods delivery need to be taken into account. Traditional supply chain logistics has problems such as requiring a large amount of manpower, inaccurate system positioning, inaccurate designed delivery routes, low efficiency, and inability to understand the status of drivers and vehicles. GIS (Geographic Information System) can assist systems in designing delivery routes, obtaining real-time locations of drivers or goods, and navigating. Therefore, in order to save manpower and ensure the safety and efficiency of goods delivery, this article used genetic algorithms to optimize and design a supply chain logistics intelligent management information system for GIS. This system had three modules, namely the client, driver, and terminal. The client mainly had functions such as publishing orders, checking the estimated arrival time of goods, and learning the real-time location of goods. The driver end mainly had functions such as real-time positioning, navigation, communication, viewing delivered orders, and monitoring cameras. The terminal mainly had functions such as viewing driver monitoring cameras and managing background information. Experiments showed that optimizing GIS could more accurately locate drivers and goods, assist drivers in navigation, assist systems in arranging delivery routes, and calculate the estimated arrival time of goods, resulting in an efficiency improvement of nearly 12%. [ABSTRACT FROM AUTHOR]