## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.4 Metaheuristics in Supply and Delivery VRPs
In the dynamic environment, a new demand might arise in addition to a prior demand, ahen all of the vehicles are at the depot. In order to satisfy the new demand, a main vehicle with sufficient capacity to supply for any shortage of other heterogeneous vehicles is proposed. A penalty is incurred for any unsatisfied demands in order to ensure that most demand is satisfied. A mix integer nonlinear model is presented with the objective to minimise the total travel time,the penalty costs, as well as the cost that is incurred, including the loading and unloading cost. In order to solve for a realistic instance, a GA is employed, where the chromosome is adapted to the dynamic demand. The problem is decomposed by iterating the GA at every period to account for any new updates. Scheduling and routing problems are studied in [85] in order to address both the evacuation and delivery operations post disaster. Two sets of vehicles are considered each, with their own respective task of evacuating or distributing relief aid. Both of the operation allows for split delivery when considering the limited capacity of both type of vehicles. The objective is to minimise the total arrival time, where the return time to depot is ignored neither that such actions are specified in the model. A small test instance is performed to compare the proposed integrated MIP model's performance with the sequential models, such that the evacuation phase serves as an input for the distribution phase. A Memetic Algorithm (MA) is adopted in order to solve for real problem instance. The chromosome is adapted to the two operations simultaneously resulting an integrated solution candidates. Meanwhile, ref. [86] proposed a model that addressed the case where insufficient supply is presented instead. Because of the insufficient supply, the fairness in distributing supply among emergency locations is maximised instead. Specifically, the maximum difference between two demand points is minimised. Furthermore, the VRPTW also minimised the total travel time. In order to establish the insufficiency of supply, each vehicle is made to only serve one location, where the demand is higher than the vehicle's capacity. Given the scenario, the optimal routing of vehicles is computed while maximising fairness in the supply distribution among emergency locations. This problem is then solved by GA, which is then evaluated and verified by a small test instance. Ref. [87] focused on distributing aids while using a vehicle that could originate from any parking area. Here, the parking area is not treated as depot, whereby the vehicle upon arrival will not immediately return. Instead, the vehicle upon the completion of task will resume a new task once ordered. Thus, a multi-trip operation is allowed. The goal is to minimise the total operation time, including the travel time, loading time, and unloading time. An Enhanced Monarch Butterfly Optimisation (EMBO) is adopted, where the modification is made on the crossover operator as well as the self adaptive strategy in order to address the problem of the solution being stuck at the local optima. The results are compared with the original algorithm (MBO) as well as seven other intelligent algorithms. A set of random generated test instance is developed in order to validate the model and solution showing better performance as compared to the original MBO and other algorithms compared. A Fuzzy Low Carbon Open Location Routing Problem (FLCOLRP) is considered in [88], which focuses on the carbon emission while performing emergency relief distribution during a disaster, such as earthquake. In this problem, multiple distribution centres are located to serve multiple demand nodes through different types of vehicles with different capacities. The objectives are to minimise the total travel cost, total carbon emissios, as well as delivery time. The formulations of the model are done by first considering the multi-objectives from which adequate constraints are derived.