Sustainable Supply Chain Optimization: A Breakthrough in Swarm-based Artificial Intelligence
Khaoula Khlie, M. Aruna, Zoubida Benmamoun, Widi Aribowo, Mohammad Javad Dehghani · Engineering Technology & Applied Science Research · 2025
Traditional Supply Chain Management (SCM) often faces challenges such as high risks due to the lack of accountability and transparency. Optimization algorithms are essential to improve green manufacturing operations. This study introduces the Dhole Optimization Algorithm (DOA) to solve a green lot size optimization problem. DOA is mathematically modeled in two phases: (i) exploration based on simulating the attack of dholes toward prey and (ii) exploitation based on simulating the chase process between dholes and prey. Real-world data are transformed into an optimization function aimed at minimizing costs and CO2 emissions. The DOA model was applied to determine the optimal lot size, balancing cost and sustainability. Comparative experiments with 12 established metaheuristic algorithms showed DOA's superior performance. The proposed algorithm was implemented in 10 case studies, and the results show that it performed more successfully than competing algorithms in all of them. This study demonstrates that not only does DOA optimize better but also reduces environmental impact, offering a promising solution for green manufacturing and sustainable SCM. This is a novel approach to lot-size optimization and highlights DOA's potential for future research and applications in the field.