A multi-objective supply chain model for disaster relief optimization using neutrosophic programming and blockchain-based smart contracts

Efficient supply chain models are crucial for ensuring swift medical intervention and the timely delivery of essential supplies in disaster management. This study focuses on optimizing disaster relief efforts in meteorological disasters, specifically flash floods triggered by cloudburst events. We propose a multi-objective supply chain model that minimizes both cost and time during emergencies by employing drones for rapid response and delivery to inaccessible areas. The model leverages Dijkstra's algorithm to identify the shortest emergency routes and integrates Neutrosophic Compromise Programming (NCP) and the Weighted Sum Method (WSM) to optimize drone deployment for cost-effectiveness and timely intervention. Pentagonal Type-2 Fuzzy Variables (PT2FV) manage uncertainty and accurately represent real-world disasters. The study also introduces a smart contract framework to enhance transparency and accountability in logistics and rescue operations. These smart contracts govern the assignment of drone-based delivery tasks, ensuring that supplies are optimally allocated and transported via the most efficient routes. The system verifies task completion and maintains a transparent record of the logistics process. The robustness of the model is validated through sensitivity analysis, while the smart contract system is confirmed through unit testing, demonstrating its reliability under varied conditions. This work aligns with Industry 5.0, integrating human-centric decision-making, drones, intelligent systems, and blockchain-based smart contracts to automate and effectively manage disaster, facilitating seamless collaboration between humans and machines.