## 4 Review of Literature
### Rescue in Routing Optimisation Problems
#### 4.3.4 Metaheuristics in Rescue VRPs  
A Large Neighbourhood Search (LNS) is used in [150] in order to address the critical time in achieving optimal ambulances routing during critical event of rescuing injured patients. In this problem, the injured patients consist of those heavily (red code) and slightly (green code) injured. These two groups of patients will be treated in hospital or on field, demanding different courses of actions. With the aim to minimise the weighted sum of the latest service times of both red code and green code patients, considerations, such as hospital capacities and priorities of the two groups of patients, are taken into consideration. The service is considered to be complete if the red code patient arrives at any hospital or the green code patient is treated with first aid. The service time addresses the preparation time for a red code patient for transportation to the hospital. Meanwhile, the service time for a green code patient is denoted as the treatment time at the scene. The problem can be considered to be a multi-depot with multiple hospitals equipped with homogeneous ambulances. The solution is addressed in two mathematical models with two index decision variables and three index decision variables, respectively. The former aims to reduce the burden of computation. A LNS is applied in order to solve for the complex problem in a fast manner and robustly. Two initials solutions were obtained from the insertion and constructive heuristics, leading to the excellent quality of solutions. No existing benchmark is available for the problem addressed. Hence, a large set of instances is generated to investigate the performance of the models with better performance in favor of the two indexed formulation model. Additionally, ref. [151], in their work, also focused on the ambulance routing problem through a minor extension of basic VRP. The deterministic problem aims to minimise the total travel cost, which is limited by each ambulance's budget for operations. Based on the emergency medical services to victim rescued or Points of Care (PoC) and the number of ambulances, a feasible solution is obtained respecting the capacity and limitation constraint imposed by the model. A GA was implemented to solve for near optimal routing. The test case based on the information that was obtained from a public hospital in Jendouba, Tunisia was adopted where the occurrence of a disaster, such as floods or winter storms are common. The SAR mission utilising UAVs as working robots with battery limitations is studied in [152]. The limitation is handled through locating the charging station, where the UAVs is required to pause their activity upon reaching critical battery level. Hence, both the location of the charging station and the trajectories of the UAVs are optimised in this problem. In order to reduce the computation burden, a two-layer level of grid map is utilised to address the trajectories as well as placement of the charging station. A GA is applied, where genes represent mission points in the sequence of the order of the genes that need to be visited, while the chromosome is evenly divided to accommodate multiple UAVs. Based on the computation, a single battery limitation can indicate the final visit that a robot is able to make. In this problem, the completion time and the operation cost, as well as the distance between the charging points, are minimised, while a penalty is incurred for violating the energy limitation via the weighted sum method. The solution method is evaluated via Monte Carlo simulation in Matlab. Furthermore, a Gazebo robot simulation is applied to evaluate the performance further. The problem of road damage and congestion during a disaster is addressed in [153] by incorporating the short term traffic flow prediction. This unique solution approach consists of multiple solutions and modelling stages.