A Web-GIS framework for efficient store allocation and routing using metaheuristic approaches

In recent years, in-person shoppers have increasingly demanded systems that minimize both travel costs and product prices. This research aims to design and implement an open-source, web-based location-based service (LBS) system to assist customers in locating stores that offer desired products at the lowest overall cost. The study focuses on Tehran, Iran, as a case study area. Unlike existing LBS and vehicle routing studies that focus on delivery or single-destination navigation, this work introduces the first open-source Web-GIS framework specifically designed for multi-stop, price-sensitive in-person shopping trips, a significantly underexplored consumer segment in dense urban environments of developing countries. The system utilizes open-source technologies to reduce development and operational costs. To optimize the shopping route, a heuristic store allocation method is proposed to reduce computational complexity from factorial O(n!) to polynomial O(n * m), where m is the average number of stores per product. Subsequently, the Travelling Salesman Problem (TSP) is solved using a Genetic Algorithm, leading to a significant reduction in total travel distance. In one test case with six product locations, the optimized route reduced the distance from 195 km (random order) to 146 km. Finally, for turn-by-turn navigation, the A* algorithm is employed, showing a 70% average reduction in response time compared to Dijkstra’s algorithm, with only minimal accuracy differences. The results indicate that the proposed framework effectively reduces shopping costs and improves efficiency for urban users. Moreover, the use of open-source Web-GIS technologies ensures scalability, maintainability, and low-cost implementation for future developments.