Efficient Strategies for Unmanned Aerial Vehicle Flights: Analyzing Battery Life and Operational Performance in Delivery Services Using Stochastic Models

In the rapidly evolving field of autonomous unmanned aerial vehicles (UAVs), commonly referred to as drones, there is burgeoning interest from sectors such as logistics, online retail, and government agencies. The impetus for this interest is largely due to technological progress and innovation. This paper presents a model based on stochastic Petri nets (SPN) for evaluating the operational efficiency of drone delivery systems, with an emphasis on the completion times for tasks executed by either individual or multiple collaborating drones. Recognizing the limitation of drones’ operational duration, chiefly hindered by battery life, the paper suggests the creation of strategic charging stations and the adoption of cooperative delivery tactics as potential solutions, albeit with inherent costs. Two SPN models were developed within this study: one depicting solo drone missions and another depicting cooperative missions with package transfers between drones. These models facilitate the examination of several performance indicators, such as Utilization Level, Mean Mission Time, Delivery Rate, and the probability of delivering a set number of packages within a designated period. By utilizing Design of Experiments (DoE) methods for sensitivity analysis, the study pinpoints essential factors that impact the performance of deliveries and offers strategies for the enhancement of drone delivery networks. The paper concludes by asserting that strategic placement of charging stations coupled with collaborative delivery efforts can effectively mitigate the limitations posed by drones’ battery life, thereby augmenting the overall efficacy and feasibility of drone-based delivery services. The SPN models provide a robust predictive framework for drone delivery performance, aiding in improved planning and allocation of resources. The findings underscore the advantages of cooperative drone operations in urban logistics and call for additional research on the optimization of charging station placement and the refinement of collaborative drone strategies.