## 2 Literature Review and Conceptual Context
### Situation-Aware Route Planning
#### 2.2.3 Dynamic Vehicle Routing Problem  
Most dynamic vehicle routing problems (DVRP) include traffic updates, since it is a complex dynamic factor and can have non-recurrent scenarios, several stochastic incoming customer orders, or a combination of both. Including stochasticity in a VRP can yield optimized vehicle routes when presented with new information. On the other hand, it is beneficial to the supply chain's stakeholders to have a planned set of routes beforehand. DVRPs present ambiguous benefits, which depend on the situation, resulting in heuristics specific to the problem. These solutions are presented in this section, and a summary can be found in Table 4.  
New customer orders are common in recent DVRPs. However, a study implemented order cancelations as well by using the Hybrid Neighborhood Search (HNS) heuristic and the Variable Neighborhood Search (VNS) to refine the solution space [56]. Since the methodology accepted changes in the customer list throughout the day, heuristics were used to insert information online and to perform a re-optimization while the vehicle was en route. In addition, fuzzy time windows were added as the quantification of the time window interval studied a constraint and service level. The short computational times show that the algorithm is appropriate for real-time scenarios in logistics. Another study also highlighted the importance of service level as responsiveness [57]. The objective was to optimize the responsiveness, not the total distance of the vehicle. The authors restarted the optimizer, Ant Colony Optimization, when new information was added to the problem. The insertion procedure only started when the ACO provided a significantly longer solution. The improvement on the ACO algorithm was a pheromone conservation tactic, where valuable information is passed on from one optimization to the next (i.e., the attractiveness of a path is not erased in the following matrix but stored, which diminishes the optimization time). This study yielded a fast heuristic for changes made en route, considering constraints such as time windows, heterogeneous capacitated fleet, and split delivery. Still, the scene where it was applied was too small to be applicable in a real-life situation. The effectiveness of this study in a large scenario is unknown. Agent-Based Modeling was also used to develop a simulation with two types of agents (trucks and retailers) interacting with each other when new customer information was available [59]. Truck agents performed route optimization using Particle Swarm Optimization, depending on retailers' new demands and urgency levels. Retailers, on the other hand, had time windows and a three-degree lateness tolerance that defined the urgency level. This study showed by simulation that the higher degree of dynamism in the routes, the longer the distance traveled by the vehicle. Furthermore, it showed that ABM is an appropriate tool to validate the environment in which different stakeholders operate for last-mile delivery. Customer satisfaction is another common objective for DVRP. An article studied the total cost of distribution using an improved Wolf Pack Algorithm, which uses the characteristic of cooperative hunting of wolves [63]. This improvement increases the communication between artificial wolves, which results in a higher global knowledge and exploration ability. In addition, they considered dynamic changes in traffic flow and fuzzy time windows. This improved algorithm uses less computation time due to the reduced number of iterations needed and high efficiency, converging into the optimal global solution in less time than the genetic algorithm. Instead of fuzzy time windows, regular time windows are not as easily related to customer satisfaction or service level. However, research still includes them in DVRP. A framework included this constraint in a DVRP with travel time uncertainty due to