| dc.description.abstract | Drones have witnessed significant advancements in the field of logistics following the COVID-19 pandemic, demonstrating their increasing proficiency in this domain. The widespread adoption of drones for business purposes by numerous countries highlights their pivotal and unstoppable role in logistics. It is evident that drones are poised to become indispensable for transportation in the near future. To effectively meet the growing demand for logistics services, drone logistics operators must expand their fleets and diversify their service offerings. By leveraging different types of drones for transportation, operators can not only provide a wider range of services but also optimize task allocation, save costs, streamline scheduling, and mitigate over-specialization. Currently, many operators have already commenced the utilization of drones for logistics transportation. Planning and scheduling a diverse mix of drones with varying models in advance can not only ensure seamless coordination among drone operations to meet the surging demand but also enhance the overall service quality, benefiting both operators and users.
This study adopts the perspective of drone logistics operators and aims to develop a comprehensive model for mixed drone fleet logistics transportation. The model takes into consideration crucial factors such as battery limitations and real-world flight conditions. The primary objective is to devise a scheduling and planning model that encompasses all the tasks involved in daily operations. By drawing upon concepts from space-time network flow, the study strives to minimize daily costs using mathematical theories and incorporating practical constraints such as flow conservation and battery limits to accurately emulate real-world conditions. Given the inherent complexity of the problem, which falls under the category of NP-hard problems, the Lagrangian relaxation method and CPLEX are employed as solution strategies. To assess the effectiveness of the proposed model and solution approach, a variety of random test cases of different sizes are generated for rigorous analysis and testing. Additionally, sensitivity analysis is conducted on crucial variables to obtain insightful results and recommendations, thereby refining the model′s performance and suggesting optimal strategies for mixed drone fleet logistics transportation. | en_US |