## 6 VRPs under Real-Time Constraints  
At this point, we have seen how taking factors such as uncertainty (simheuristics), integration with ML models (learnheuristics), and synchronization issues (discrete-event heuristics) into account is crucial in dynamic systems. The next category aims to cover real-time constraints. Nowadays, we count on many devices and technologies that can provide data, such as current fleet locations, new customer requests, or the state of the traffic, which are useful for performing real-time decisions. The main applications for VRPs with these constraints are dynamic fleet management (real-time dispatching of vehicles), vendor-managed distribution systems (replenishing inventory to avoid stock-out), couriers (collecting parcels and sending them), rescue and repair service companies, dial-a-ride systems (services for transporting people between two nodes), emergency services (relocating idle vehicles, optimizing routes), and taxi cab services [67]. When it comes to VRPs with real-time constraints, some of the approaches in the literature can be summarized as shown in Table 1.  
As can be concluded from Table 1, the most common real-time constraint is that related to customer requests, although variations in travel times are also tackled, but with less frequency. On the other hand, the methodologies used to solve these problems are various, but all of them agree on the most critical point: It is necessary for the implemented solution to be extremely fast and effective. The family of agile optimization (AO) algorithms [75] is a good choice for real-time decision making, since they are able to obtain extremely fast execution times. The main idea behind these algorithms is the parallelization of heuristics that are usually easy to implement, randomized, and count with a small number of parameters, thus easing the parameter fine-tuning process. The effectiveness of these algorithms has been shown in the literature, as they are able to provide quality solutions to complex problems of different natures, including large-scale dynamic or uncertain conditions that are present in the real world, such as those involving traffic, vehicle location, unexpected demands, disruptions, etc. Work regarding AO includes [PERSON] et al. [25], where a fleet of homogeneous vehicles had to satisfy customers' demands in a two-echelon network. This paper extended a constructive heuristic to a biased-randomized algorithm with parallelized runs. It was shown that this approach could improve upon the original version by about 10% without increasing the required wall clock time. In [PERSON] et al. [76], the authors tackled the integration of IoT analytics into AO problems. By employing an AO biased-randomized heuristic, the goal was to solve a waste collection problem, which was modeled as a dynamic team orienteering problem with mandatory visits. It was shown that this approach outperformed the solutions provided by its original version. Similarly, [PERSON] et al. [77] provided a comprehensive review of the state of the art of the Internet of Things in intelligent transportation systems (ITSs). In this context, challenges were identified for cloud computing, fog computing, and edge computing. An AO-based