## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.4 Metaheuristics in Supply and Delivery VRPs
This partial solution, now reduced in complexity, is solved by CPLEX for an optimum sub-solution. This sub-solution is then iterated further by SA with another crossover mechanism, besides local search to reach a near optimal solution for the whole problem. We also consider [92] as a VRP, although the proposed multi-period relief distribution model also consists of scheduling problem. Here, the problem of ideal depot location as well as the availability of transport network post disaster are being addressed for the humanitarian operation of relief distribution. Apart from a set of multi-periods and introduction of penalty update function, as well as a specified objective function, a standard modelling approach for VRP is adopted and referred to in [93] with shelters, depots and road network along with capacitated vehicles with the aim to minimise the costs and penalties. Because of the complexity of the problem, a GA is applied with a two tiered chromosome representing two decision variable: a sequence of shelter and numbers of relief distribution. The solutions could be at different lengths, where the mutation and crossover are sufficiently applied over a thousand generations that stem from 250 population size. The model and solution is then validated while using a case study based on the Nankai earthquake affecting the Minato ward of Osaka. Ref. [94], on the other hand, raised the issue of a possible assault when transporting relief aid due to the scarcity of supply in a post disaster area where people are desperate for emergency supplies. The problem is modelled as the last mile distribution problem with known numbers for relief aid prior to delivery, which involves a network of unavailable roads due to damages. Six criteria are considered as the objective of the model, namely: time, cost, equity, priority, security, and reliability. In order to balance all six objectives, compromise programming is applied instead of the typical approach of assigning weights on each of the objectives. An ACO is adopted to solve the problem that is based on the novel representation of ants, representing both vehicle and aid kits and effective pheromone mechanism, among four other types of pheromones. Here, the solution is constructed based on both the pheromones and a greedy approach to diversify the solutions. Using the effective pheromone and greedy function in an additive equation leads to more accuracy when constructing a solution, as opposed to the conventional multiplicative equation between the greedy and pheromone function. The model is tested on real cases of the Haiti earthquake 2010 and the Niger famine 2005. Most recently, ref. [95] complemented the critical delivery for temporary shelter construction in times of disaster by including drones in the Multi-Objective VRP with Time Window and Drones (MO-VRPTWD) with the objectives of minimising the total energy consumption of both the trucks and drones, as well as the total number of trucks. The energy consumption is reflected by different capacities that each truck carries, the distance travelled, as well as the different speed of each drones and the distance travelled while operating. As commonly applied in various literature, a penalty is also imposed should a time window be violated. An Improved Artificial Bee Colony (IABC) is proposed to solve the problem. An improvement is made in the bee scouting strategy to improve the global search. [PERSON]'s benchmark is applied to test the model and superior performance is observed by comparing the proposed solution with TS, GA, and VNS.