## 1 Introduction  
Sustainable cities and communities are identified as one of the 17 sustainable-development goals proposed by the UN [1]. The achievement of this goal requires the significant renewal of the way urban space is perceived. In this context, smart cities are envisaged as the main driver of such a transformation. Advanced materials, sensors, electronics, and networks embedded in our physical and social systems constitute the core concept of a smart city, allowing for more sustainable and resilient societies [2]. Smart cities are an ever-changing environment [3], which aims to provide high-quality life to its citizens supported by the advancement in information and communication technology and the integration of the Internet of Things (IoT). As a result, new mobility modes are considered such as ride-sharing [4] or the incorporation of electric vehicles [5]. Unmanned aerial vehicles (UAVs), commonly known as drones, have attracted significant attention in the last decade, given their potential for new applications [6]. For example, in the case of smart cities, they can work as aerial base stations, either collecting data from mobile and ground sensors or serving as sensor-mounted aerial platforms [7]. In that way, an on-demand data service can be realized, covering a larger number of sensors and users. A major disadvantage of these devices is their limited energy capacity, which adds a certain complexity to implementing these types of services, especially when serving remote sites [8]. Several operational research lines arise to cope with this limitation: (_i_) the optimal drone placement (ODP) problem, which guarantees the coverage of static or dynamic targets minimizing the required energy [9; 10]; (_ii_) the problem of selecting the optimum charging station once the drones have finished their tasks [11]; and (_iii_) the route-planning problem and all its variants [12; 13]. Considering that a fleet of drones can work in a coordinated manner to achieve a common goal, such as the aerial surveillance of an extended area, the problem of coordinating their individual paths accounting for their available energy can be envisioned as a team orienteering problem (TOP). The objective of the TOP would be, following the case of the aerial surveillance, to maximize the area covered by the fleet of drones. Therefore, the efficiency of the fleet of drones is maximized. The drone scheduling problem (DSP) is an extension of the TOP that considers multiple depots (stations) [14]. Figure 1 depicts the conceptual framework of several operational research problems regarding drones. Smart cities allow for real-time data, nevertheless, their potential can come to fruition only if combined with real-time decision-making. This is not only the case for drones, where optimal routing should be established accounting for the dynamic conditions (e.g., dynamic targets) but also for other types of autonomous vehicles working collaboratively, which might be increasingly frequent in transportation and mobility activities. Traditional optimization approaches cannot handle real-time conditions effectively [15]. Therefore, new optimization approaches are required to deal with large-scale systems in'real-time' (e.g., less than one second). The development of efficient solving approaches becomes incredibly challenging given the large-scale problems that real applications involve, and the fact that the TOP is NP-hard in nature like the most of routing problems [16]. In order to tackle the challenges imposed by smart cities, this paper proposes the use of _agile optimization_ algorithms [17] to solve TOPs with dynamic conditions in'real-time', i.e., below one second of wall-clock time. In this context, optimization algorithms are redesigned to become (_i_) extremely fast to support real-time decision-making; (_ii_) extendable to adapt parallelization techniques; (_iii_) flexible to handle different problems; (_iv_) parameter loss to avoid parameter fine-tuning; and (_v_) dynamic to rerun as new data become available (re-optimization).