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

Abstract

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 Nondominated 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.