## 1 Introduction
Because this study does not consider small UAVs, such assumptions are not valid, and the study has to create offline mission plans with flight plans for each UAV where it needs to be sent to the authorities in advance to get the approval to be executed afterward. In offline planning, collision avoidance could be done by predicting potential collisions where online planning reacting strategies are utilized. Moreover, the techniques to avoid collision change when the fleet of UAVs are flying in free space vs. dedicated corridors. Depending on the context of the problem, different strategies have to be adopted for collision avoidance [30]. This study focuses on offline mission planning of UAVs by predicting the collisions in advance and planning missions ensuring collision avoidance, where the principle of prevention is used as a guarantee of UAV deadlock-freeness. The payload capacity and the flight range of the UAVs are related to the energy capacity, and these aspects are vital in mission planning [31; 32]. Studies have proposed to segregate the whole network area considering the UAVs' relative capabilities and proposed clusters in the area to reduce the problem size [33; 34; 35; 36]. In this study, customer nodes are clustered to reduce the complexity of the network, where each cluster has a set of feasible UAVs fleet routings, and accompanying schedules are provided. Weather conditions are pertinent to UAV routing because wind speed and wind direction could agitate the travel speed of the UAV, and the air density, appeased by the temperature in the atmosphere,affects the energy consumption in UAVs [9] where cold temperatures may adversely affect battery performance [9]. The majority of the current state of research has paid less attention to weather factors and ignores the impact of weather on performance [14; 33; 37]. The current literature has sporadically considered wind conditions on energy consumption and the use of that information in planning the missions [9; 38; 39]. The studies have assumed constant wind speed and direction [38] and have used linear approximations for energy consumption, giving less focus on the impact of weather [9]. Because of the UAVs used in this study, linear approximations are not coherent. The weight of the UAVs used here are heavier than the UAVs used in existing research because the studies have stated that their models are not reasonable when the weight of UAVs increases [9]. Accordingly, the non-linear models proposed in the literature are used in this study in calculating energy consumption considering weather conditions [24]. The mission-planning problems addressed in this study cover different layers of system architecture, which include fleet level where the fleet is managed to provide delivery service and the platform level where it focuses on the individual functioning of the UAVs. Thus, the contributions of this study cover both fleet and platform levels as shown in Figure 1. The goal of this study is to fill the gap in literature addressing UAV fleet-mission planning in changing weather conditions, maximizing customer demand satisfaction in a given time period. Because of the novelty of the problem and the fact it covers different layers of decisions, the initial objective of this study is to find a reasonable way to solve the problem. It is difficult to claim that any proposed method is the best way to solve a problem, but this method allows future research to find better approaches to solve the problem. The remainder of the paper is organized as follows: Section 2 provides the problem formulation and the composition of the central problem concerning UAV fleet-mission planning with changing weather conditions. Section 3 presents models for the problem of UAV mission planning. Then, the already stated problem formulated with a declarative solution approach aimed at addressing problem composition is considered in Section 4 An illustrative example of the proposed approach is discussed in Section 5, and the concluding remarks are presented in Section 6.