An evolutionary analytics model for enhancing food distribution through two-tier routing

Food insecurity remains a global challenge that demands efficient and equitable logistical solutions, especially in the distribution of food aid. This study addresses the Two-Echelon Vehicle Routing Problem (2E-VRP) in foodbank logistics by proposing a novel multi-objective optimization framework. The model combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with k-means clustering, a rare approach in food distribution logistics, to optimize route efficiency. The first echelon involves transporting food from a central depot to intermediate agents using trucks, while the second echelon involves delivering food from agents to beneficiaries using vans. The primary objectives are to minimize total delivery distance and reduce delivery time, while considering fleet capacities, time window constraints, and ensuring fair distribution. Using spatial and demand data from Medan, Indonesia, the study evaluates the model’s performance, computational efficiency, and sensitivity to logistical factors such as fleet size. Results show that clustering improves route compactness and reduces travel distance, especially in large-scale networks. However, it increases computational time, highlighting a trade-off between solution quality and complexity. This research offers a scalable, data-driven framework for foodbank logistics, contributing to sustainable logistics and providing an innovative solution for optimizing food distribution in both urban and rural settings. • Address multi-objective food delivery using an evolutionary clustering-based optimization model. • Enhance routing efficiency by integrating clustering in two-tier distribution networks. • Validate the model using spatial and demand data from a real-world foodbank network. • Reduce travel distances in dense areas through improved spatial route formation. • Reveal trade-offs between optimization quality and computational effort in large networks.