## 5 Current Trends
Figure 6:1 Breakdown of Reviewed Papers based on Model Characteristic. Figure 6:2 Selected Humanitarian Operation based on RVRP. Figure 6:3 Selected Humanitarian Operation based on Multi-Objectives VRP. Figure 6:4 Breakdown of VRP for Supply & Delivery Operation based on Modelling Aspect. Figure 6:5 Breakdown of VRP for Rescue Operation based on Modelling Aspect. Figure 6:6 Selected Humanitarian Operation based on Deterministic VRP. Meanwhile, from the solution approach perspective, only two papers solved their VRP through the ML solution approach (Figure 26), each for supply and delivery operation and rescue operation, followed by 16 papers that adopted the hybrid solution approach with breakdown of 11 papers for supply and delivery operation and three each for evacuation and rescue operations (Figure 27). In addition, 35 papers employed the exact solution approach with slightly more than half addressing supply and delivery operation, followed by seven papers addressing rescue operation, and nine for the evacuation operation (Figure 28). Meanwhile, 25 papers adopted the heuristic solution, which saw an almost equal share of both supply and delivery operation and evacuation operation with 12 and 11 papers, respectively, followed by only two papers for rescue operation. Finally, 50 papers applied a metaheuristic to solve the VRP with both evacuation and rescue operation having an almost equal share of papers, with 10 and nine each, respectively, leaving supply and delivery operation with 31 papers. When looking at the heuristic and metaheuristic individually, the breakdown percentage could be observed in Figures 29 and 30, respectively. Interestingly, the results show that the number of works adopting hybrid solution is only slightly lower when compared to heuristic solution in supply and delivery operation (Figure 31), while metaheuristic continues to dominate the works that address VRP in disaster applications, followed by the exact solution approach (Figure 32). Furthermore, these numbers echoed the trend in [15] in terms of ranking by mostly applied solution approaches if hybrid solution and ML are excluded. The only  
Figure 6:7 Selected Humanitarian Operation based on SVRP. Figure 6:8 Selected Humanitarian Operation based on DVRP. difference is the reduced dominance of metaheuristic usage among the reviewed papers. That being said, this review focuses on the specific application of VRP in humanitarian operations, as opposed to the broad field of applications covered by [15]. Figure 33 provides the trend of solution approaches based on the selected humanitarian operation. Figure 6:9 Selected Humanitarian Operation based on Hybrid Solution. Figure 7:0 Selected Humanitarian Operation based on Machine Learning (ML) Solution Approach. Figure 7:1 Selected Humanitarian Operation based on Exact Solution. Figure 7:2 Selected Humanitarian Operation based on Metaheuristic Solution. Figure 7:3 Selected Humanitarian Operation based on Heuristic Solution. Figure 7:4 Breakdown of VRP for Supply & Delivery Operation based on Solution Approach. Figure 7:5 Breakdown of Reviewed Papers based on Solution Approach. When looking into each of the operations individually, the supply and delivery operations show a clear dominance by the metaheuristic in terms of solution approach with roughly 42 percent of the papers belonging to this class. This is followed by the exact solution approach with approximately 26 percent of the papers, leaving both heuristic and hybrid solutions with almost an equal share of percentage. As can be expected, the ML solution approach only accounts for about one percent when compared to all solutions applied. In details, the finding is illustrated in Figure 7:6 Likewise, a similar trend could be observed for the rescue operation in Figure 34, leaving the evacuation operation showing a different trend. Interestingly, here, the heuristic is applied more, albeit only three percent more, than the metaheuristic, followed by the exact solution that roughly triples the value of hybrid adoption in percentage.