## 4 Review of Literature
### Rescue in Routing Optimisation Problems
#### 4.3.2 Exact Methods in Rescue VRPs  
The problem of overwhelming demand for emergency medical help that is snowballing during the time of disaster is studied in [142]. A bi-objective location allocation and ambulance routing is addressed in order to determine the location, the number of temporary emergency shelters, and the optimal route for ambulance in rescuing the victim that need medical assistance. In this problem, the demand is divided into two types of patients that are coded with red and green colour. The red type patient is considered to be critically injured and should be transported to the temporary emergency station. The green code patient, on the other hand, receives treatment on site. The objective is to minimise the complete service time of treating both red and green code patients, as well as to minimise the cost of setting up the temporary emergency stations. By first linearising the non linear constraint, the \(\epsilon\) constrain method is then applied to solve the problem, where the test instance is based on the case from Tehran city. Ref. [143], in his work, made use of the public monitoring system service provider, known as the Golden Eye, to provide proof of real time monitoring of the transport network condition to guide the emergency vehicle into the heart of disaster affected area during the post disaster event. Based on the real time data pre- and post-disaster, a custom AI makes a prediction on the traffic and road condition in guiding the decision makers. Moreover, the internal Golden Eye program analysis is utilised to fill in the blind spot due to damaged monitoring equipment on site. Based on the existing expert input on damage and possibility of repair, an optimal route for the emergency vehicle is then dynamically computed while using an improved Bidirectional Dijkstra optimal path selection algorithm. For a path that failed to be monitored due to faulty equipment, a prediction is then made that is based on data pre- and post-disaster at a regular interval. As a result, these predicted paths could also be included when computing an optimal path. The rich topological demographic of Fujian province in China was considered when verifying the model. The problem of emergency vehicles dealing with road conditions, such as the connectivity reliability of road, the computed rescue time travel, as well as the index road security is considered in [144]. For the travel time, the model is built considering the reduced capacity of a link as well as the increased flow of the link due to a sudden disaster or emergency event. Two correction coefficients are introduced to incorporate into the existing Bureau of Public Road (BPR) function formulation to address the sudden nature of a disaster onto the traffic travel time. The connectivity reliability is defined as the measure of the possibility of the link being used under specified conditions and times. The third variable addressed the safety of the emergency vehicle while in transit during disaster event, especially those occurring in intersection, which is frequently the case. The Pareto optimal solution is applied in order to address the multi-objective problem. The depth search is adopted to search for feasible solutions from which the sub-problem (based on the three consideration's value function) is updated. Furthermore, the alternate solution is obtained through the non-dominated sorting method that is based on the predefined conditions. Eventually the problem and solution method is validated based on the traffic accident that occurred at the intersection of Yunnan Road and Beijing Westroad, Nanjing China under heavy downpour. Ref. [145] focused on the efficiency of rescue routing by integrating two separate models namely the Data Envelopment Analysis (DEA) model with the Incomplete Linguistic Group decision Constraint Cone (ILGCC) and an IP model to compute for optimal rescue routing. The former model incorporated five decision variables to determine the rescue efficiency.