## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.4 Metaheuristics in Supply and Delivery VRPs
The objective is to minimise the total cost, which includes the penalty for delivering insufficient demand, penalty for servicing more than demand needed, as well as transportation and operation costs. The first problem is tackled by converting the multi-depot VRP into multiple single VRP through the nearest assignment and average distance method. Meanwhile, the second problem is solved through their proposed improved version of GA. The tests are then conducted based on two numerical examples where the first consist of a simulated instance and the latter is based on the water-logging disaster in Changsha City, Hunan. Meanwhile, an interesting approach of the ACO can be found in [71], where the problem of vehicle allocation and dynamic demand are considered. The problem placed the government as the decision maker that needs to decide on routing solution, depending on loading capacity of the vehicle, the time taken for vehicles to gather at a distribution centre, as well as the time that is taken for a vehicle to reach disaster areas. These three variables changes over time turning this problem into a dynamic VRP with changing demands and numbers of vehicles based on information updates. The problem is formulated as MDPs, where the objective is to minimise the total longest transportation of all vehicle per each dynamic phase. To allow for the continuous update of information, dynamic solution approach is needed in order to compute the route allocation for each vehicle online via the dynamic programming approach. This new update is embedded in the route allocation algorithm, where the assigned route is computed while using the ACO algorithm. A case study based on the earthquake event in Lushan County in 2013 is adopted in order to validate the model and solution. A SA is applied in [72] in order to address a multi-objective blood donation problem. The problem is in two-fold: blood mobile location problem and the shuttle routing problem. These problems addressed the issue of perishable platelets, uncertain blood donor's arrival, as well as supply chain planning, which involves both facility location problem and VRP, whereby maximising the blood collection while minimising the cost is difficult. The first problem of locating blood-collection mobile is solved by the crisp multi-objective linear model, obtained through the conversion of the respective multi-objective fuzzy programming model. The output of the blood donation mobile location problem is then applied to the VRP (second model) that is solved by the SA in an attempt to maximise blood donation while minimising the cost that is incurred by both blood mobile and blood donation shuttle. Several test instances are utilised to validate the model and solution method. The first model is solved by CPLEX, while the second model is solved by the proposed SA. The results shows applicability of the proposed solution. The problem of relief distribution under disruption risk through the LRP is addressed by [73]. Particular to this problem, the risk of route disruption, the number of vehicles, as well as temporary distribution centre's capacity is considered in the first 72 h of emergency response while trying to maintain fairness in delivering relief aid. Multi-commodity relief items are considered to be delivered from Temporary Distribution Centers (TDCs) to Victims Temporary Residence (VTRs) via heterogeneous vehicles on the ground. In order to account for the disruption, each respective disruption from the route, vehicle disruption, as well as the TDC capacities disruption would incur an additional cost, which would need to be minimised as part of the multi-objective problem. The operational cost which includes the setup of TDC cost, the transportation cost, the violated time window penalty, as well as unmet demand penalty are also minimised. Furthermore, the risk of disruptions is measured via the measured of Conditional Value at Risk (CVaR), which is easy to incorporate in a LP model for optimisation purposes as compared to the measure of Value at Risk (VaR).