## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.3 Heuristics and Local Search in Supply and Delivery VRPs  
As an alternative to the exact method approaches, solving VRPs through a heuristic and metaheuristic approach, could be considered to be a typical choice. This includes the problem of supply and delivery for HL where various heuristic approaches are proposed. Ref. [51], for example, applied heuristic to solve for the large instances of their Last Mile Distribution Problem (LMDP). This problem looks into the effect of considering multiple and conflicting objectives, which are the efficacy, efficiency, and equity, which are appropriate in assessing effective distribution during a chaotic event of disasters. The efficacy is regarded as the amount distributed per node during the time spent, while the efficiency is measured based on the time of delivery and finally the equity is measured based on the disparity among nodes in terms of efficacy. In order to investigate the effect of applying these objectives on the resulting routes, the problem is restricted to one trip operation per vehicle while allowing split delivery among vehicles. Because of this limitation, no vehicle carries capacity enough to fully served one demand point while the total demand is sufficiently supported by the depot. Two decision variables are proposed to govern the routing as well as the allocation of supply, respectively. The effect on route structure is observed based on preferred objectives among three different combinations of three objectives, computed together, which derived the model further into three models. The problem that is formulated with the set partitioning formulation is solved by a commercial solver for small instances and heuristic based on the framework of Greedy Random Adaptive Search Procedure (GRASP) for large instances. The first two models are solved by heuristics that are based on the constructive phase and improvement phase at each iteration for a set number of repetitions. The constructive heuristic is based on an insertion heuristic of randomly restricted selected node candidates. This solution is then improved by performing a local search. This solution approach, however, could not be applied to the model that focuses more on the equity issue of distribution. For this model, a two-phase heuristic approach is utilised. These two phases comprise of setting partial allocation to each node at the initial phase, and the second phase considers adding a node to the route or adding the supply in the initial phase to serve the remainder of the demand. The results concluded with several practical insights, such as that all three objectives could be achieved better by utilising a large number of small vehicles as opposed to commercial practice, which prefers large carriers. This is due to the flexibility and mobility of small vehicles in accessing remote destinations as well as the ability to traverse the affected transportation network. This also improved the equity, as more demands could be covered and served more fairly. However, this comes with non-excessive addition of travel and operation costs that are considered to be reasonable in times of urgency. Ref. [52] proposed a DSS incorporating both the optimisation platform and simulation, making use of available graphical database. It addressed the coordination problem between private and relief organisation during the response phase of a disaster. In this problem, the lead time between request and delivery is minimised, depending on the road availability. A MIP model is proposed for this problem. The transfer points, demand points, supply points, as well as depots are depicted as nodes or vertices. To solve the model, a Star algorithm that is piloted by a heuristic method is applied, resulting in a near-optimal route. Meanwhile, the facility location problem is addressed by TS. By solving the location problem, the average service time (from request to delivery point) is minimised through relief supply coordination.