Optimizing food distribution in Villa El Salvador's community kitchens using binary integer programming and vehicle routing CVRP

Modesto-Romero B.; Quintana-Macukachi A.; Vergara-Cordova J.; Ninaquispe-Soto M. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.2574

Resumen

This research focuses on improving food supply logistics through mathematical programming applied to community kitchens in Villa El Salvador, applying Binary linear programming and vehicle routing techniques (CVRP). The main objective is to improve distribution efficiency, reducing distances, delivery times and maximizing the use of available resources. Two complementary mathematical models were developed. The first one, implemented in PYTHON, allows to optimally assign the 150 canteens to one of the eight distribution centers (DC), considering the distances and the service capacity of each DC. The second model, implemented in Python with ORS and Pulp CBC, generates efficient daily delivery routes, considering that each truck has a capacity of 2 tons and a maximum working day of 8 hours. The model simulates multiple trips per truck returning to the distribution center for reloading. Since it is a single monthly delivery per canteen, the results obtained allow planning in advance the necessary delivery days, guaranteeing the fulfillment of demand without exceeding logistical capacities. The results show that Model 1 assigned all 150 community kitchens to the 8 distribution centers, covering 100% of demand with a total accumulated distance of 141.36 km. Model 2 generated 30 optimized routes with a total distance of 106.12 km and 87.57 effective working hours, equivalent to approximately 11 working days. These results confirm that the proposed approach is operationally viable, improves equity in food distribution, and has the potential to scale to the full 242 community kitchens in the district, strengthening food security for vulnerable urban populations.

Instituciones: Universidad Nacional Tecnológica de Lima Sur; Universidad Privada del Norte