## 4 Review of Literature
### Evacuation in Routing Optimisation Problems
#### 4.2.3 Metaheuristics in Evacuation VRPs  
The problem of evacuation of victims with disabilities is addressed in [131]. This problem is classified as an Overburdened VRP (OBVRP), where not all victims can be evacuated, due to the overburdening of resources. This is reflected in the objective function that tried to minimise the total number of unevacuated victims. Consequently, typical swapping heuristic operations are no longer feasible in addressing the problem. This is due to the fact that each possible route must now contain unevacuated victims. To deal with the problem, different versions of ACO extensions that consist of Min-Max Ant System (MMAS), Elitis Ant System (EAS), as well as neighbourhood ant heuristic and greedy ant heuristic were employed. Each of them corresponds to creating upper and lower bounds for the pheromones, providing an emphasis on a strong solution from the start of the search serving as a good initial solution. Because there is no available and existing standard benchmark for validating the model and solution, newly generated instances are derived from [132]. The obtained results highlighted the importance of proper parameter selection for the algorithm. Ref. [133], on the other hand, addressed the dynamic VRP, where the evacuation routing from a local shelter to a long term safe settlement is updated at each 15-minute interval. Here the travel time taken along the route, the capacity of vehicles, and the numbers of evacuees left at the local shelter are dynamically updated in order to capture the essence of dynamic changes during disaster. The route planning need to be executed in such a way that minimised the total travel time which results in reduced evacuation time. The two-stage solution procedure assists the evacuation routing, where the clustering of shelters to each depot is performed in the first stage. The second stage executes a routing optimisation via a SA with an initial solution obtained by the nearest neighbour heuristic. Stage 1 and 2 are both updated at each 15 min. interval, eliminating empty local shelters and updating vehicle capacities. The transportation network of north west of Tehran was utilised as a case study to test the model where the static and dynamic model's solutions are compared. A Multi-Objective Multi-Trip CVRP (MO-MTCVRP) is discussed in [134] when addressing the evacuation operation with limited resources when considering multiple, yet conflicting, objectives. The assumption is made that these limited resources should be used as many times as possible during a critical time. Furthermore, a fast response in the last mile problem is ensured by the objective of minimising the maximum latency, which reflects the waiting time of the last affected victim in the operation. Additionally, the total cost and number of vehicles are minimised in order to ensure an efficient relief operation. To solve the complex problem, a Multi Start algorithm with Intelligent Neighbourhood Selection (MSINS) is proposed. Here, a number of neighbourhood structures undergo the two phases of a Multi-Start Algorithm, where the feasible solution is first created and later improved. Based on the principle of optimality that was introduced in the previous study, a multi objective local search is then executed in the two phases of a Multi-Start Algorithm. This solution approach is compared with the NSGA-II algorithm and the results indicate that the proposed solution outperformed the NSGA-II in both computation time and solution quality. Ref. [135], on the other hand, raised an interesting notion of using the autonomous heterogeneous bus fleet to evacuate victims from an affected area to a safer location. The objective is to minimise the total travel cost that consists of the cost incurred by the travel time as well as the cost related to the risk while evacuating victims. In this problem, a conventional formulation of VRP is presented with the exception of a heterogeneous fleet.