## 6 Future Direction and Research Gaps  
The survey findings indicate that the metaheuristic will continue to dominate the solution approaches for the VRP, in particular for the application of humanitarian operations. This is due to the tendency of the resulting model becoming more complex, as more important criteria should be addressed to describe the chaotic situation of the humanitarian operations. Naturally, metaheuristic would be the obvious choice to solve these complex models in a practical sense. To echo the work of [15], a considerable number of RVRPs have started to emerge, especially in this specific field of application. It is expected that more RVRPs would surface within the field ensuring prolonged relevance of metaheuristic solutions. The advancement of computing power, on the other hand, will ensure that the exact solution remains relevant, as the strong appeal for obtaining an optimal solution rather than settling for a near-optimal solution will always be tempting. For this very reason, the growing number of adoption of an exact solution will continue to increase proportional to the advancement of computing power. In this paper, the reviewed literature are differentiated between cooperative mechanism among solution approaches and hybrid solution approaches, where one solution approach might be incorporated within another solution approach. Nonetheless, the hybrid solution approach is slowly gaining traction due to its relevance in overcoming the limitation of existing solution approaches. The dilemma between optimality and practicality remain prevalent within the context of optimisation. The idea of obtaining an optimal solution for practical practices will continue to be desired. Rather than waiting for the advancement of computation in the near future, making use of existing solution approaches towards reaching this goal is also an obvious step. Such efforts would surely give rise to more hybrid versions of solution approaches. Meanwhile, the natural modelling of both dynamic and stochastic by MDP, which often leads to the ML solution approach, could no longer be ignored. In representing the chaotic situation in pre- and post-disaster settings, the uncertainty of various parameters will remain an important issue. Furthermore, the changing variable within an interval is commonly observed and utilised in humanitarian operations. Looking at the trend, it will soon be a must that DSVRP be addressed for all humanitarian operation purposes, should effective delivery, evacuation, and rescue operations be executed. As such, it is anticipated that more hybrid versions of ML would emerge in addressing the curse of dimensionality haunting the ML solution approach. In terms of DSS, various online tools, such as GPS, BIM, and GIS, will soon be widely incorporated, as can be seen in [52, 111, 123]. This would ensure the continued relevance of DVRP. Perhaps more online tools covering various aspects of humanitarian operations will soon emerge, giving rise to more potential research to cater for this new development. Moreover, the advancement of computation also allows for ML to advance further in assisting humanitarian operations in terms of vehicle routing. Such a decision support system would perhaps be able to imitate the human decision-making process, as observed in the game of Go. Finally, we curiously note the abundance of work covering the routing problem in terms of delivering critical aids to victims. When compared to such problems, evacuation routing problems and rescue routing problems are rather scarce. In the RVRP setting, such problems are even rarer. Furthermore, most of the reviewed papers addressed the response phase of DM, leaving the other three phases with, at most, three papers reviewed out of 123 papers. A similar observation could be made for papers addressing different disaster types. Research addressing slow onset disaster in this scope is still lacking. From the modelling perspective, the deterministic problem is addressed the most.