Real-Time Medical Aid Delivery: A Digital Twin Approach with Dynamic Vehicle Routing Problem

Recent advancements in logistics systems have been motivated by the increasing need for quick and effective delivery of critical goods, especially medical aid. Delivery delays in circumstances like disaster response, emergency medical care and immunization distribution can have detrimental consequences. Medical aid delivery has unique logistical challenges, especially in real-time scenarios. The urgency of healthcare needs, the perishable nature of medical supplies, and the unpredictable nature of traffic necessitate highly adaptable and effective routing solutions. This study proposes a novel framework that integrates Digital Twins (DTs) with Dynamic Vehicle Routing Problem (DVRP) models to enhance real-time medical aid delivery. By integrating DTs, real-time data, and AI-driven optimization methods, this framework aims to improve the efficiency, and adaptability of medical logistics systems. Experimental results demonstrate that the proposed approach has achieved excellent results in terms of request satisfaction rate, total distance, and vehicle utilization, highlighting its potential to enhance healthcare services in emergency situations.