## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.5 Hybrid Optimisation in Supply and Delivery VRPs  
The number of literature employing hybrid methods in solving supply delivery VRPs seems to be almost comparable to those employing heuristic methods. This is probably related to the fact that, in this specified problem, the hybrids applied are often those that combine different types of heuristics and metaheuristics or merging both heuristic and metaheuristic methods into a new version of a hybrid method. However, some problems require the merging of exact solution with either the heuristic or metaheuristic method. Ref. [96] addressed the problem of HL in terms of routing and allocation in the response phase of disaster by taking the various capacity of heterogeneous vehicle, storage capacity. as well as vehicle's working time into account. The storage capacity of the distribution points is considered while being served periodically, making it a VRP problem with dynamic demand. Due to critical nature of the delivery, a penalty is incurred for any delay based on certain penalty factors. The main objective is thus to minimise the penalty incurred thereby ensuring satisfactory delivery. The problem also seeks to minimise the unbalance distribution, making it a multi-objective problem. A hybrid solution is proposed in order to solve the problem. This algorithm executes an exact solution approach while performing perturbation in SA, ensuring the simultaneous optimisation of routing and allocating demands. The sub-problem is formulated as integer programming and it is solved using the Gorubi software. Additionally, Cumulative Capacitated VRP (CCVRP) versions are optimised through hybrid methods in [31] that looks into multi-trip CCVRP (mt-CCVRP). Ref. [31] addressed the problem with large scale disaster, where the road could be compromised and affected for which helicopters would be an ideal choice to make a first evaluation on the affected area. Because of critical arrival time imposed on each helicopter, the sum of arrival time is reduced when compared to conventional CCVRP problem that minimises cost and distance. The problem is solved by a Multi Start Iterated Local Search (MS-ILS) with a Variable Neighbourhood Descent (VND). Each initial solution obtained by greedy randomised heuristic is optimized by VND within the working framework of MS-ILS. Through this mechanism, the exploration of MS-ILS is effectively utilised, while the VND acts as an exploitation tool for each of the initial solutions. A similar structure is applied in [97] that studies the relief operation during the response phase of disaster. The authors went further to consider in detail the different priority of supplies, various injury level of evacuees which corresponds to their priority as well as the different roles of relief workers along with their different priorities. The objectives of the problem are to minimise the total unsatisfied demand, non-evacuated evacuees, as well as non-transported relief workers. Because of the thorough detail taken by the author, such a complex problem could only be solved by a specific heuristic tailored to the requirements detailed for larger instances. A greedy heuristic and a local search are both applied in an algorithm consisting of four phases. Furthermore, the second phase of the algorithm is executed through an exact solution approach. Based on the humanitarian operation addressed, ref. [97] could be considered as both applied to supply and delivery as well as evacuation processes. In [98], the problem of cumulative Multi Depot VRP (cum-MDVRP) is solved by incorporating within the conventional framework of an ACO and a 2-Opt heuristic. The authors addressed the problem of the post disaster relief operation during the response phase where multi-depot facilities allows for multi-trip among vehicles. The objective function reflects the urgency of time where the delay time of total relief delivery per trip is minimised excluding the trip to depot.