## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.5 Hybrid Optimisation in Supply and Delivery VRPs
The problem is formulated as a MIP model, for which a hybrid solution approach, known as multiple round ACO (mr-ACO), is proposed. A 2-Opt heuristic is applied to each the best selected and constructed path created by the ants to increase the speed of the convergence of the ACO. A Tabu list is created specifically to prevent the usage of non-permitted move encountered by the ants while constructing the routes within the ACO iterations. A more stable solution is achieved with the proposed hybrid when compared with existing heuristics. Meanwhile, through the formation of hybrid Cellular Automata (CA) and a GA in [99], a complex stochastic VRP is solved, addressing the stochastic road travel time as well as the demands. In this study, the demands and travel time during the chaotic event of a disaster are assumed to be unknown. One depot is assumed with homogeneous vehicles with the goal of minimising the total arrival time. In the proposed CGA, an additional step is added after the selected chromosome undergoes mutation, where it is further mutated into a cellular cell to expand the exploitation capability of the CGA. As a result, the convergence efficiency of the CGA is increased. This also ensures that the constraints are satisfied before the CGA resumes for the next generation. Ref. [100] argued that the probabilistic theory should not be applied to represent the degree of belief that some uncertain events might occur. Instead, the uncertainty theory would be a better choice. Based on this argument, an uncertainty theory is utilised in order to represent two models that addressed the routing problem in distributing urgent medical provision to critical destinations. In this problem, the demands and delivery time are uncertain and their expected values are minimised or maximised in order to obtain an optimal solution of the models. A hybrid solution that is based on a proposed uncertain simulation and a GA is presented. The proposed uncertain simulation is utilised to compute the expected values, the inverse uncertainty distribution as well as the uncertainty measure from the chromosomes that are generated by GA. A numerical example is presented, showing the applicability of the proposed model, which leads to improved fairness in delivery as opposed to the conventional model that relies heavily on operational time. The problem of emergency grain delivery in post disaster is considered by [101] where three objectives are optimised through the proposed model and hybrid solution. The satisfaction level is maximised based on the arrival time of the vehicles. The total cost consisting of demand cost, penalty cost, and travel cost are to be minimised. Additionally, the sum of transportation time is also minimised. A time window is assigned to each demand point, resulting in a penalty cost should the time window be violated. This problem is solved by an improved hybrid solution Immune ACO (IACO), where the Immune Algorithm (IA) is applied in order to distribute the pheromones based on its fast global convergence and random mechanism, while the solution is optimised based on the exploration ability and positive feedback by the ACO. The non-dominant sorting algorithm is applied to design the immunity of the ACO in order to address the convergence and diversification of Pareto optimisation. A numerical experiment that is based on [PERSON]'s benchmark data is conducted in order to compare the hybrid performance, which outperformed the results obtained from ACO, IA, and GA. The problem of distributing large relief material via Ship Routing Problem (SRP) in the event of disaster is addressed in [102]. The problem is regarded as an open Split Delivery VRP with the addition of varying ship's speed that decreases over time, depending on the geographical condition. The problem is formulated as a mixed non-linear programming model and the objective is to minimise the total travel time.