چكيده به لاتين
Drones have entered many fields with their wide range of applications. They have gained popularity in the market by effectively executing certain control and operational processes performed by their commercial counterparts. Companies involved in the supply chain use drones to facilitate a wide spectrum of processes. By employing drones, many activities that were previously time-consuming and labor-intensive can be carried out efficiently, cost-effectively, and significantly safer. This thesis focuses on the integrated route planning of ground vehicles and drones in two scenarios, considering realistic assumptions to improve the transportation system. The major difference between the two scenarios lies in the type of drone usage. The first scenario utilizes drones for operational purposes, while the second scenario employs surveillance drones to enhance transportation. As the limited traffic zone is often not considered in the literature on drone routing problems, this scenario aims to address this gap, particularly in Tehran, where such a zone could lead to increased costs. In the first scenario, the vehicle routing problem with drones is developed by considering the limited traffic zone. The objective is to minimize the costs associated with the entry of ground vehicles into the limited traffic zone while serving the customers in this area as much as possible through the use of drones. Additionally, since the literature on drone routing problems pays less attention to the possibility of disruptions during drone flights, this scenario takes into account the likelihood of drones being affected by weather conditions and charging state of drones using a type-2 fuzzy controller. In the developmental scenario, decisions regarding the flight or non-flight of drones are made based on the probability of drone disruption. The use of simultaneous surveillance to improve security in the routing of valuable goods has also garnered attention from researchers. Therefore, in the second scenario, drones are employed to ensure the security of valuable goods during transportation. Given the inherent nature of valuable goods, they are constantly exposed to risks that pose a threat to their transportation, making the preservation of their security crucial.Therefore, in this thesis, a new integrated model for routing ground vehicles and drones is presented, utilizing drone surveillance to ensure the security of transporting valuable goods. In this scenario, the risks arising from intentional disruptions are analyzed in two stages: planning and execution. In the planning stage, the risk is estimated based on historical data, and routing is performed with the goal of minimizing the risk cost and transportation cost. In the execution stage, drones monitor the generated solution routes in real-time. Ground vehicles can only enter a link if the risks have been evaluated by drones within a specific time window before their entry. Additionally, in this scenario, if there is an overlap in the time windows of ground links, drones have the possibility to visit multiple ground links, simultaneously. If a suspicious agent is identified by the drones, a dynamic replanning is performed with the aim of eliminating the risky agent. Furthermore, to improve this problem, a developmental scenario with considering charging stations is presented. To optimize the first scenario and its developmental scenario, a novel algorithm combined with a reinforcement learning approach is proposed. Additionally, to optimize the proposed problem in the second scenario and its developmental scenario, developed metaheuristic algorithms are designed. The efficiency and effectiveness of the proposed algorithms are evaluated in both small and large- scale dimensions of the two scenarios. The obtained results demonstrate the quality and competitiveness of the proposed algorithms. Moreover, a sensitivity analysis is conducted to evaluate the influential parameters of the problem. Finally, real case studies are examined to investigate the proposed scenarios.
