## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.4 Metaheuristics in Supply and Delivery VRPs
Adapting the CVaR into the objective function, the model is then linearised to address the nonlinearity of the initial model. The problem is then solved by GA based on the new proposed representation of the chromosome specific to the problem. Better results are obtained when compared to the optimal solution that was obtained by CPLEX in terms of computation time. An integrated LRP is presented in [74] in order to govern the UAVs application in relief distribution. The optimal location of a warehouse is determined, followed by the routing of UAVs with the objective of minimising the humanitarian operations' cost as well as the number of hubs that are utilised by the UAVs. Interestingly, in the model is the introduction of a penalty for any loss of life due to the delay in delivering relief aid. Besides that, the operational costs for setting up a depot as well as the operational cost of UAVs are accounted for in the objective. The small instance of the model is solved by CPLEX using the branch and bound algorithm. However, to solve for the real instance, the bi-layered Large Neighbourhood Search (LNS) is employed with the inclusion of Informed Rapidly explored Random Tree (IRRT), such that the path is re-planned and improved. The existing benchmark instances are based on the damages and relief demand in the central region of Nanto. Meanwhile, a multi-conflicting optimisation criteria regarding the last mile distribution of relief aid is addressed in [75], where the issues of safety, efficiency, equity, and effectiveness are considered to allow for more a realistic representation of the problem. Specifically, the problem search for an optimal route to deliver relief aid from a distribution centre to a set of demand nodes. Here, the multi-modal transport, multi-depot, multi-loading, as well as split delivery are considered. The problem is formulated as a compromised programming for the last mile distribution, where non-dominating criteria are achieved as a solution with the optimal trade-off between all the criteria. The utilisation of metaheuristic is warranted due to the complexity of the problem. Evolutionary algorithm is avoided, since slight changes in potential solution might render the solution infeasible. The Greedy Randomise Adaptive Search Procedure (GRASP) is implemented to verify the model based on the test case of the 2010 Pakistan Flood, 2010 Haiti Earthquake as well as the 2005 Niger famine. Ref. [76], on the other hand, focused on the multi-objective, multi-period, multi-commodity open LRP where special care is given to the condition and the short term repair of the affected road while distributing supplies and repair crew during the post disaster phase. In this problem, all the cost associated with setting up distribution centre, travel cost as well as repair cost is minimised. Furthermore, the maximum travel time of the route is also minimised while maximising the quality of the route by ensuring that the service of repair equipment is done along the way. What is unique in this problem is the fact that the safety of crewman is considered within the problem model, which is rarely the case in literature. Because of the multiple objectives needed to be solved by the problem, a multi-objective metaheuristic that could achieve Pareto optimal solution is desired. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilised, along with the multi-objective PSO (MOPSO), to solve the problem. The LRP with Simultaneous Pickup and Delivery (LRPSPD) under uncertainty of victims to be evacuated during disaster is solved by [77]. The objective is to reduce the total cost of operations, which includes the transportation cost, the setup of an emergency logistics centre cost, and fixed cost for vehicles. The problem is also treated as a multi-depot problem, as is usually the case with LRP. A numerical experiment is conducted by varying the Decision maker Preference Index (DPI) and the resulting objective value is observed based on the risk that the decision maker is willing to make.