## 4 Review of Literature
### Evacuation in Routing Optimisation Problems
#### 4.2.3 Metaheuristics in Evacuation VRPs
The two associated costs are then combined into one cost that is based on different weights assigned to each cost. An evolutionary algorithm is proposed to solve the problem. A case study is used to evaluate both the modal and solution based on Hillsborough County, Florida, where hurricane Irma occurred in 2017. From the results, an encouraging number of cost reduction is obtained as compared to the manual evacuation effort. The evacuation problem due to typhoon is considered in [136] by looking into the secondary re-occurrence and the chain effect of the typhoon. The evacuation process is divided into two phases. In the first phase, the evacuees are transported to the emergency shelter, while, in the second phase, the injured victim will be transported to the hospital. Heterogeneous vehicles are made in order to scatter and their priority are based on their tasks given. Furthermore, the secondary disaster, such as the resulting mud slide and flood, are reflected through the damage of the road affecting the travel time of the vehicle. This phenomenon is denoted by the road impedance coefficient which is directly correlated to the damage of the respective road. The aim of the model is to minimise the time that is spent by vehicle throughout the operation. A matrix encoding based GA is proposed in order to solve the problem. The problem model and solution are then evaluated by a simulation that is based on the real Meranti Typhoon that occurred in 2016, affecting as far as Xianmen, China as a super typhoon. Meanwhile, ref. [137] addressed the issue of solving hte minimax problem for evacuating evacuees due to a disaster in two stages, resulting in two objective functions. The first stage involves transporting evacuees from an affected area to a temporary shelter resulting in an objective of minimising the maximum evacuation time of evacuees. The second stage involves transporting the victims from a temporary shelter to a safe area where from the perspective of decision maker, the total evacuation time for all evacuees is minimised. No direct connection is given between the affected areas, the temporary shelters and the safe areas. Solving the nonlinear minimax optimisation problem is challenging, where a unique solution is sought for in this specific problem as compared to other problems in the literature. The reason for this being that a multiple solution namely the routing guide would confuse the evacuees who are obtaining the guide from the decision maker. As such, a lexicographic minimax MIP model version of the problem is considered, where the tabu search is employed. The model was then validated by the real network of Honolulu, Hawaii while using two test scenarios where the demand of evacuees is different. The efficiency of evacuation by simultaneously optimising the location of evacuation hub as well as the routing of public transport via two layers target model is studied by [138]. A public transit network is optimised for evacuation by introducing the reversible lane for the bus transit as well as the private vehicles for evacuation. Assumptions were made, such that the capacity of evacuation hub as well as the demand is based on refugee numbers. It is also assumed that there is no delay in the evacuation process. In this problem, any suitable trunk road can be considered as the bus reversible lane increasing the capacity of the flow in order to improve the evacuation. Moreover, the far right of the lane is reserved as the reversible bus lane, so the activity of evacuation can be done back and forth. The problem is modelled as the min cost flow model, where the total travel distance and the total evacuation time are minimised. A multi-objective GA is then performed, where the objectives are to improve simultaneously. A case study of the Beilun district in Ningbo is adopted for the case of typhoon evacuation in order to validate the model.