An Intelligent Decision Support Framework for Competitiveness Optimization in Indian Shipping and Port Management

Abstract:
Data-driven intelligence is essential for Indian ports to compete by minimizing the usage of resources, maximizing operational efficiency, and making more informed decisions in ever-evolving sea conditions. The current research formulates an Intelligent Decision Support Framework (IDSF) based on Reinforcement Learning (RL) and Analytic Hierarchy Process (AHP) to achieve adaptive optimization in port and shipping operations. AHP layer produces understandable weights by carefully ranking competitiveness factors such as berth utilization, logistics expense, and ship turnaround time. The best-working practices, including historical and real-time data, are iteratively learned by the RL agent, designated as an MDP, by taking these weights as input. Experimental verification with actual data from Indian ports proves that the proposed method has an efficiency accuracy of 97.6%, a decrease in operation delays by 22.4%, and a significant increase in competitiveness indices in comparison with traditional optimization procedures. The platform promises scalability, flexibility, and openness, while at the same time offering a revolutionary technique for maritime decision intelligence and sustainable port management.