## 1 Introduction  
The immense development of unmanned aerial vehicle (UAV) capabilities has promulgated its influence in many domains, such as defense, search and rescue, agriculture, manufacturing, and environmental surveillance [1; 2; 3; 4] to execute complex industrial functions. Fleet-mission planning for UAVs in the context of materials delivery has become a major research topic [5; 6; 7; 8] and could be treated as an extension of the vehicle routing problem (VRP) [9] which belongs to the class of planning problems that are NP-hard [10; 11]. Various objectives could be used in UAV mission planning such as reducing individual UAV costs, enhancing its profit, increasing safety in operations, reducing lead time, or increasing the load capacity of the entire system [12; 13]. UAV routing in 3-D environments has different degrees of attractiveness when evaluated against multiple decision criteria, which relates to finding the sequence of waypoints that connects the start to the destination locations [14; 15; 16; 17]. Existing studies on UAV routing for transporting materials and surveillance [9] have given less attention to changing weather conditions and the non-linear fuel consumption behaviors of UAVs [18]. This study addresses the problems of routing and scheduling of a UAV fleet, considering the changing weather conditions. The focus of the study is the solutions that allow for finding feasible, collision-free flight plans ensuring non-empty batteries for a fleet of UAVs. This approach maximizes the satisfaction of the orders from given customers. The proposed declarative solution approach enables one to state a decision support-driven reference model aimed at the analysis of the relationships between the structure of a given UAV-driven supply network and its potential behavior, resulting in a sequence of sub-missions following required deliveries. From this perspective, the results of this study fall within the scope of the research presented in the papers [19; 20; 21; 22; 23; 24]. Numerous parameters and constraints influence the decision criteria for UAV fleet mission planning. Typical limitations of UAVs in transportation systems include the limited flight range, which depends on the battery capacity, the payload of the UAV, overlapping air corridors designated for UAV movement and the technical specifications of UAVs [8]. Furthermore, the decision space consists of aspects which include routing and scheduling in the 3-D environment, the carrying payload of the UAVs and collision avoidance with moving or fixed objects, energy consumption affected by the changing conditions like wind speed, wind direction and air density [8]. Thus, these various interdependent aspects emphasize the intractability of UAV fleet-mission planning because it is challenging to develop models considering all the influencing aspects together. There is little knowledge on how the changes in these influencing parameters affect the solutions, even concerning deterministic approaches. For instance, the collision avoidance solutions differ regarding fixed obstacles vs. moving flying obstacles and single obstacles vs. many obstacles. Studies in the literature have used methods for UAV mission planning where the routes have been derived by satisfying the UAV dynamics using linear approximations for energy-consumption models [25; 26]. Certain existing models have not considered all the significant physical properties related to UAVs and have used approaches such as VRP with time windows (VRPTW) [27; 28]. Depending on the context of the problem, mission planning can be done online or offline. Most of the online mission planning has been done assuming that the UAVs can detect the obstacles using sensors to avoid collisions [29]. Because of the recent advances in collision-avoidance technology, most small UAVs can sense the air traffic and alter flight altitudes or turn to avoid collisions. Studies regarding routing problems in communication networks do not explicitly avoid collisions between UAVs [11].