IT-Enabled Route Planning: K-Means Clustering and GA–SA Metaheuristics for High-School Brochure Delivery

The distribution of promotional brochures to thirty-one public high schools in Surabaya is modeled as a vehicle routing problem and solved entirely with information-technology tools. Geospatial coordinates are processed with open-source Python libraries, K-Means clustering partitions delivery points into three spatially coherent groups, and each cluster is optimized by stand-alone Simulated Annealing (SA) and a hybrid Genetic Algorithm–Simulated Annealing (GA-SA). Computational experiments executed on a standard workstation show that GA-SA reduces total travel distance by roughly 15 percent relative to SA while adding only modest runtime. The study illustrates how readily available IT resources can deliver actionable route improvements for logistics planners and provides a reproducible baseline for future research on cluster-first routing heuristics.