## 4 Review of Literature
### Rescue in Routing Optimisation Problems
#### 4.3.5 Hybrid Methods in Rescue VRPs  
The Urban SAR Team Deployment Problem (USAR-TDP) is addressed by [158]. For cases, such as rescuing victims trapped in a damaged building due to the disaster, it is appropriate to model the problem with stochastic demands, travel time, as well as service time. This reflects the nature of such key characteristics that cannot usually be known a priory. As the USAR operations prolong, the situational awareness of the post disaster environment increases while the survival of the victims on the other hand reduces to reflect a more realistic scenario. The approximation technique can usually be applied to reduce the Multi-stage Stochastic Programming (MSP) into a single stage. The MSP is decomposed into a sequence of inter related two-stage stochastic programming with recourse where the previous decisions and information of the former two-stage programming with recourse is used for the subsequent two-stage problem. A column generation method is devised in order to reduce the large two-stage stochastic programming at every decision epoch where the inclusion of each possible tour for a team is regarded as one column. The decomposed sub-problems are then solved through VNS and, if needed, through an exact solution from the branch and price method. As such, we regard the solution as a hybrid solution strategy. The earthquake that struck Port-au-Prince in Haitian Capital in 2010 is taken as a case study to validate the model applied.  
The problem of rescuing injured victims in the time of a disaster is studied by [159] in the form of non fix Multi Depot Pickup and Delivery VRP (MDPDVRP), where the survival rate of the victim is maximised. In this problem, simple assumptions are taken such as the homogeneity of the vehicle with fixed speed and capacity. An Emergency Reply Center (ERC) plays the role of assigning vehicles to patients to be rescued, depending on the distance. Furthermore, ERC also provides critical information regarding the road condition in the aftermath of the disaster. In contrast to the typical approach of maximising the service, the focus here is to maximise the number of saved life. The model is solved by a proposed hybrid GA, where the chromosome signifies the nodes and the order of the genes represents different hospitals. An improvement is made on the feasible solution from the permutation through the local search. A case study that is based on Tehran city is applied in order to validate the solution and model. GA and the proposed hybrid GA are both compared where better performance could be seen in the proposed solution.  
The problem of rescuing victim during storm flood disaster in urban cities is considered in [160], where the chance constraint programming specifying a confidence level under which the stochastic travel time constraint is guaranteed to hold. In this problem, a multi-objective with an assigned weight is addressed to minimise the total travel time budget of all vehicles as well as the maximum travel time budget of a single vehicle. A simple model is assumed, where there is only one depot, and that the vehicle could only visit a site once with no return trip is allowed. A lognormal distribution is applied to model the uncertain travel time based on the real data obtained. In this problem, the vehicle unloads a rescue operative as well as facilities before resuming its route onto other disaster sites. As such, this problem could also be considered as a supply and delivery problem.  
To solve a realistic problem size, a hybrid of ACO-TS is proposed, where the addition of TS is applied in order to improve the solution that was obtained by the ants before the pheromone is updated. By doing so, the immature local optima obtained by the ACO is avoided. The model is then validated through a real storm flood that occurred in the urban area of Guangzhou, China.