Computational Intelligence and Sustainability of Indian Logistics in Comparison with The Artificial Intelligence and Traditional Optimization Paradigms

The Indian logistics sector is presently undergoing a massive structural and digital transformation; market projections imply that the logistics sector could grow if it is now at more than USD 429 billion by 2034. This study discusses the pivotal shift from the conventional mathematical optimization models to Artificial Intelligence (AI) and Computational Intelligence in reaching sustainability of the environment and operational efficiency for the economy in rapidly growing scenario. The research holds paramount importance because the logistics sector is traditionally a costly and polluting sector which accounts for about 13-14% of the overall greenhouse gas emissions in India. This high emission profile has been attributed to major factors which include heavy dependence on diesel-operated trucks, choking urban environments, and incomplete road networks.The paper makes use of a multi-dimensional case study approach with a view to studying industry leaders (for example, Delhivery and TCI Express) and the performance of the systems in a comparative sim model set up within the unique context of Indian urban environments. A comparative analysis methodology is used to compare effectivity of the Mixed-Integer Linear Programming (MILP) with popular models of Machine Learning (ML) and Reinforcement Learning (RL). Key results show that the application of AI in route optimization can cut fuel consumption by 18.7% and total logistics costs by 22.4%. Furthermore, AI models show a 20USD-26USD% reduction in the emission of carbon emissions during the high traffic disruptions which is a major improvement relatively to static traditional paradigms which don't hold up in the dynamic traffic conditions of major Indian cities.