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

Mohamed Hassouna; Amal Elsayed  Aboutabl; Naglaa Mohamed              Diaa; Riham Younis  Haggag

doi:10.5614/itbj.ict.res.appl.2026.20.3.8

Authors

Mohamed Hassouna Department of Business Information Systems, Faculty of Business Administration, Higher Technological Institute, Next to Small Industries Complex, Industrial Area 2, 10th of Ramadan City 11111
Amal Elsayed Aboutabl Department of Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Ain Helwan, Helwan, Cairo 11795
Naglaa Mohamed Diaa Department of Business Administration, Faculty of Commerce and Business Administration, Helwan University, Helwan, Cairo11795
Riham Younis Haggag Department of Business Information Systems, Faculty of Commerce and Business Administration, Helwan University, Helwan, Cairo11795

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2026.20.3.8

Keywords:

EHO, geospatial data, green AI, hippopotamus optimization, multi-objective optimization, sustainable supply chain, vehicle routing problem

Abstract

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 (? = 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.

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