Conference Agenda

Unmanned Aerial Vehicles (UAVs), with their flight capabilities such as range, endurance, and altitude, have increased the capabilities of both military power and organizations performing operations such as search and rescue. UAVs can easily accomplish tasks that are difficult and unsafe for conventional aircraft, such as discovering or destroying chemical, biological, and nuclear facilities or highly defended combat platforms on land or at sea. Effective planning for the use of resources with high technological capabilities is as important as developing and employing them. The problem addressed in this study assumes the presence of threatening elements in various regions with specific probabilities. The study involves deploying a UAV fleet to search these regions within a network, each with different levels of search challenges, all within a constrained operational timeframe. The search for detection in each region follows the assumptions of random search, where the time to detection is a random variable of exponential distribution with a certain mean. The mathematical model developed for the problem attempts to create a search plan that maximizes the total detection probability of threats continuously throughout the time from the start to the end of the operation. Within this plan, decisions such as determining deployment points (bases) for each UAV in the fleet, determining the regions to be searched and the search times for each UAV, and determining the search sequence among these regions will be simultaneously optimized. A heuristic solution approach is suggested for the instances that are large in size and need to be solved quickly.

The efficiency of last-mile delivery in medical logistics is critical, particularly when delivering urgent medical supplies such as pathology specimens, vaccines, test kits, and emergency medication. The integration of advanced technologies like blockchain and unmanned aerial vehicles, commonly known as drones, presents a promising solution to enhance these operations. Given the critical need for secure, tamper-proof data handling of medical items, blockchain technology is employed to ensure data integrity, real-time tracking, and compliance with health regulations. We introduce a novel two-echelon truck and drone routing problem model within a blockchain-managed network. This study aims to leverage the speed and flexibility of drones, combined with the security and transparency of blockchain technology, to streamline medical logistics operations and enhance the responsiveness and reliability of medical supply chains. For this problem, we develop a mixed integer linear programming formulation along with a metaheuristic algorithm designed to efficiently handle large data instances. The implementation of our model in a simulated urban environment not only shows a significant reduction in delivery times and operational costs compared to traditional vehicle-based logistics solutions but also enhances data security and improves the traceability of medical shipments, providing a reliable audit trail throughout transportation.