Multi Objective Optimization of Distribution in Supply Chains using Green AI via an Enhanced Hippopotamus Algorithm

Modern supply chains require innovative, sustainable, and computationally efficient solutions for physical distribution. This paper proposes the Enhanced Hippo Optimization (EHO) algorithm, a novel, nature-inspired metaheuristic designed to solve the Multi-Objective Vehicle Routing Problem (MO-CVRP). The (EHO) algorithm mathematically models three key hippo herd behaviors: exploration (herd positioning), defensive coordination, and strategic withdrawal, to achieve a dynamic balance between global exploration and local exploitation. By integrating real-world geospatial data from the Google Maps Directions API and customer demand modeling via a Zipf distribution (α = 0.9), (EHO) simultaneously reduces total distance, delivery time, operating costs, and CO2 emissions. The algorithm was evaluated under twenty scenarios across a network of nineteen cities in Egypt, outperforming six advanced algorithms Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), RUN, Artemisinin Optimizer (AO), and Fata Morgana in eighteen cases. The framework achieved an average reduction of 26.9% in distance and emissions, with total computational runtime ranging from 12.77 to 25.1 seconds depending on the scenario. Visual analyses, including optimized route maps and radar performance charts, confirmed the robustness, scalability, and practical effectiveness of the EHO system in transforming logistics into a driver of economic and environmental sustainability.