## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.4 Metaheuristics in Supply and Delivery VRPs  
Looking into the multi-objective VRP in this problem domain, ref. [66] proposed a delivery model that takes into account the congestion level that is based on the destruction of the road network and a chaotic environment involving the road network. The road capacity is assumed to be affected by the disaster and it can be associated with the congestion level that is related to the flow capacity of the road. Both the total delivery time as well as the total cost incurred using the weight coefficient transformation method are optimised. In addition, more priority is given according to the weight assigned. Genetic algorithm (GA) is applied in order to solve the capacitative vehicle routing problem with time window. Ref. [67], on the other hand, proposed a model that minimised the total cost of delivery and transportation, while, at the same time, maximising the satisfaction degree of the recipient based on the anticipation, endurance, and failure level. The problem is stochastic as the actual road capacity is uncertain, modelled through an uniform distribution as compared to its designed fixed capacity. The satisfaction level is expressed in a trapezoidal fuzzy function that is based on the anticipation degree, endurance degree, as well as the failure degree. The travel time is computed based on the Bureau of Public Road impedance function. A Simulated Annealing (SA) is employed to solve the problem. Ref. [68] is of the opinion that selective VRP is more appropriate in representing the routing problem with uncertainties as opposed to conventional VRPs. They argued that hard choices are sometimes unavoidable and need to be made, regardless of the extenuating circumstances. This translates into the fact that not all of the nodes could be visited, thus the Selective VRPs. They proposed three selective VRP formulation based on fuzzy Stochastic Vehicle Routing Problem (SVRP), reliable SVRP, as well as robust SVRP. These proposed models fill the gap, where, previously, only deterministic Selective VRP are addressed. In order to solve these models, ref. [68] proposed three improved Parallel GA. The three algorithms differ from each other based on their communication and diversification strategies. Their performances, together with conventional GA, are then compared by testing them on both small and large network. Among the proposed model formulation, Fuzzy SVRP shows better potential in addressing large problems, due to the fuzzy nature of various uncertain elements in the operation. A common approach of clustering first and routing later can be seen in [69], which addressed the problem of routing vehicle to large affected destination due to disaster while having a large amount of concentrated medical relief supply at distribution centre. In this problem, a single depot and a fixed number of homogeneous vehicles are assumed with multiple affected areas for delivery distribution. Furthermore, in order to address the large quantities of relief aid, several parameters are proposed based on the quality, handling, and packaging of the distributed materials, which are incorporated into the constraint of the model. The objective is to minimise the total travel time of the delivery operation. In order to solve the problem efficiently, a K-Means clustering algorithm is applied to cluster various emergency location into groups before computing the route for each cluster while using Particle Swarm Optimisation (PSO). Ref. [70] viewed the critical delivery of emergency supplies from the supply and delivery perspective by considering the supply that is needed for each centre in order to distribute to selected emergency points. However, in disaster time, such as an earthquake, the demand is usually uncertain. This leads to penalty added should the supplies be insufficient for delivery. Strategically, the problem is divided into two, where clustering is needed to assign emergency points to each distribution centre.