 This paper discusses the problem of selecting an optimal subset of operators from a larger pool of candidates to complete a mission efficiently. It focuses on unmanned aerial vehicles, UAVs, performing firefighting tasks and compares two different approaches, one based on deterministic algorithms and another using stochastic algorithms. The simulation results demonstrate the effectiveness of both methods, showing that the stochastic approach can be used to optimize the mission while maintaining a reasonable level of accuracy. Additionally, the paper proposes a framework for UAV firefighting missions, develops deterministic and stochastic algorithms for resource allocation, and introduces time-efficient search schemes. These techniques are applicable to a wide range of UAV applications, including healthcare, surveillance, and security operations, as well as other areas where resource allocation is important, such as wireless communication and smart grids. This article was authored by Sohail Rizak, Costas Ideas, Anzarma Mood, and others. We are article.tv, links in the description below.