## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.5 Hybrid Optimisation in Supply and Delivery VRPs
A Multi-criteria Fuzzy Clustering algorithm is then proposed in order to obtain suitable clustering of the affected area based on the computation of dissimilarity of the demand points, the resulting fuzzy correlation matrix and finally clustering. A hybrid GA is presented as the solution of the model, where the Dijkstra shortest path algorithm is adopted in the decoding phase and solution construction. The solution scheme and model are then validated and evaluated through a numerical experiment that is based on 2008 Wenchuan Earthquake in China. A dynamic and stochastic last mile distribution for disaster relief response is addressed in [103]. In the final stage of distribution, the coordinator faces the challenge of high demand, due to shortage of supply at the end of distribution. Furthermore, the damage that is sustained by various infrastructure, including the road, worsens at the last stage of delivery, leading to the uncertainty of demand and travel time. This leads to dynamic routing, where a pre-computed route needs to be rerouted based on the updated info. The formulated problem aims to minimise the total travel time. The problem is solved by the proposed hybrid SA and VNS. The initial solution is obtained by first allocating the nodes to the route, which is later constructed by performing nearest neighbour insertion heuristic. In order to deal with the dynamic nature of the problem, the problem is treated as a multi-stage static problem. This means that any changes, i.e adding node to the pre-computed node will be done via the Clark and Savings algorithm. This complete initial solution is then improved by the hybrid SA and VNS, where the local search in SA is performed by stochastic VNS. A real case study based on the data of Yogyakarta Indonesia is applied and the performance of the proposed solution is compared with SA, VNS, and ACO, showing comparable results. Ref. [104], on the other hand, addressed both covering tour and the dynamic routing problem, where temporary relief shelters are erected to cover the affected disaster area while the dynamic routing aerial transport vehicle is determined in order to distribute relief aid in the aftermath of the disaster. Through the proposed model, the affected region is covered by at least one temporary relief shelter and uncertain demand, and the affected region is addressed. A virtual end node is added to the model in order to model the objective of minimising the arrival time of last assigned shelter. In solving the problem, two algorithms are applied, including the proposed hybrid algorithm. First, the known GENI based heuristic that is composed of two combined insertion heuristics is used to create route for the vehicles. Subsequently, the partition of disaster areas served by the temporary relief shelter and the assigned vehicles are determined by the hybrid Scatter Search (SS) and VNS. The local search operator is substituted with the VNS instead within the SS mechanism. Randomly generated instances are applied for a small scale problem. For a larger instance, an existing benchmark instance from [105] is modified and adopted. The hybrid between metaheuristic and metaheuristic could be seen in [106], where the problem of fairness in relief distribution in HL is addressed in order to prevent discontent outbreak, as well as to save as many victim as possible. Four measurement of fairness are considered that are based on previous studies: absolute fairness, the min-max method, fair sequence, and multi-objectives. A Peer-induced Fairness Capacitated (PFCVRP) model is proposed in order to solve the problem. The fairness coefficient is integrated in the objective function. An improved ACO based on tabu list is proposed and considered the least used vehicle rather than applying all vehicle at disposal. The initial solution from the improved ACO is used by VNS to improve the solution further and, afterwards, local search is applied, leading to a near optimal solution. The model is validated based on randomly generated small instances which are inspired by [107].