## 4 Review of Literature
### Rescue in Routing Optimisation Problems
#### 4.3.4 Metaheuristics in Rescue VRPs
The real traffic data are managed through the internet based data gathering program, as well as other existing real data gathering systems, such as the traffic control, the vehicle tracking program, and the road toll system. These rich data are converted into a fuzzy model, where the attributes or indicators are represented by the fuzzy set, whose membership degree is denoted by the value of the data attributes. Through this fuzzy information system, the traffic congestion can be predicted and applied when computing the optimal route for the emergency rescue vehicle via the guided local search with a variable penalty function. In order to validate the proposed model, a test data based on 13 of the main roads of Lanzhau is utilised. In addition, the test instance from previous literature was applied and the results were compared with different solution algorithms, including the traditional local guided search. The stability and quality of the proposed algorithm was proven as well as showing superior results when compared to the traditional guided local search. Ref. [154] focused on improving the saving of lives and reducing human suffering in the allocation and routing problem. In this problem, three humanitarian objectives are formulated and captured with regards to lifesaving utility, fairness, and delay cost. The rescue operation was considered within the first 72 h of post disaster event. Two decision variables are introduced, each addressing the allocation and routing component, respectively. In this problem, the allocation decision variable addressed the number of rescue victims that should be loaded onto the vehicle at the demand point. The objective was given to minimise the total travel time, which is also the total rescue time, in order to minimise the delay cost that is associated with the deprivation cost and maximise the lifesaving utility reflecting priority of wounded victims for efficient lifesaving. This problem was solved by two metaheuristic algorithms, namely the NSGA-II as well as the proposed NSGA-II with ACO. Both complex and simple examples are applied in order to validate both methods. In the analysis, it is shown that the proposed mutation and crossover operators lead to a more quality solution when compared to their original counterpart. The ambulance allocation routing problem was considered by [155], where the rescue operation of the patients was based on three categories, depending on the severity and the required services. In order to emphasise the urgency in saving the patients, a soft time window constraint is introduced, whereby the thresholds are based on the survival function of the patient category. A penalty was added for the late arrival and the objective is to minimise the weighted sum of latest service completion time over all patients treated, the penalty time imposed while treating the red coded patient, and the penalty time that is imposed to treat the yellow coded patient. The three different categories of patients are colour coded, symbolising non-critical patients that can be treated on the spot, severe patients that should be transferred to hospital, as well as severe patients that need to be transferred to hospital while sustaining his or her life with equipment within a certain type of ambulance, respectively. Four types of ambulance are defined, depending on the severity of patient. GA and TS are both applied to solve for the realistic problem size, due to the vast and complex problem that is being addressed. Both of the solutions are compared with a modified greedy heuristic and CPLEX solver to evaluate the results obtained. Better performance and efficiency are observed in the proposed TS method. Ref. [156] addressed the emergency relief routing for injured victims by rescuing and dispatching them to the medical centre. Two humanitarian points are considered and compared. The first point is the varying degree of injury as opposed to the assumption of all injuries are identical, which is depicted as one of the objectives.