Swiftship - AI Optimized Delivery Time-Slot Management

In today's fast-paced world, customers demand personalized, time-bound services. India Post, the largest mail delivery organization in India, currently operates with a rigid delivery window from 10 am to 5 pm, often leading to missed deliveries and customer dissatisfaction. This paper proposes a flexible, customer-concentric, time-slot-based delivery model that leverages advanced technologies such as machine learning. The model allows senders to select delivery time slots during booking and enables recipients to modify the time slots prior to the delivery date. The system uses machine learning algorithms to analyze historical data and optimize future delivery slots, enhancing delivery efficiency. The proposed solution aims to reduce missed deliveries, increase customer satisfaction, and improve operational efficiency within India Post's delivery system. Experimental results demonstrate that the Random Forest-based model achieves 79.92% accuracy in predicting optimal delivery time slots, with a strong correlation (0.97) between scheduled and rescheduled deliveries. The comprehensive evaluation includes performance metrics such as precision, recall, and F1-score across different geographical regions, demonstrating the model's effectiveness in diverse delivery scenarios.