## 6 Future Direction and Research Gaps
This could point to the missing link between the theoretical and practical side of the routing in the selected humanitarian operations, as it could be argued that the deterministic model rarely accounts for a real-life problem that is dynamic and stochastic in nature and mostly applied for theoretical purposes. Finally, as it is often mentioned in this study, the adoption of the ML approach is very much lacking as far as the research gap is concerned. This leads to some research questions for future research:  
1. Could the ML modelling and solution approaches be utilised to its full potential (overcoming the curses of dimensionality) to address a more complex RVRP in detailing accurate depiction of humanitarian operations for practical instances? 2. How to overcome the limitations of the existing solution approaches utilising hybrid solution in addressing RVRP for practical instances? 3. How to incorporate the emerging online observation and communication tools in the optimisation computation of user-friendly DSS for the applications of humanitarian operations? 4. How to incorporate as many elements of the chaotic environment as possible into the evacuation and rescue routing problems for the humanitarian operations in the setting of RVRP? 5. Could a general model be developed that applied to all disaster phases as well as disaster types? Guided by these research questions, more focus should be paid in developing a much richer VRP that incorporates as many elements as possible in the event of disasters. To this end, more work should be done in making use of the MDP modelling approach that naturally addresses the respective dynamic and stochastic elements, as would be desired for the humanitarian operations. As pointed out by [15], we have yet to see more multiple stochastic and dynamic parameters being addressed simultaneously. As such, more effort should be made to look into such problems. A richer RVRP should entail an intermodal network, as the humanitarian operations normally utilised all available modes of transportation in achieving effective outcomes. All of these qualities demanded by such RVRP would no doubt lead to a more complex model where solving it would be challenging. A hybrid solution approach could be the key in solving such a problem. Concurrently, the ML solution approach could also play the same role. Despite the potential of Deep Neural Network in handling a complex task, we have yet to see one, such as in [161], addressing the RVRP in the settings of the humanitarian operations. Hence, we should also expand the optimisation world of OR beyond the exact, heuristic, metaheuristic, and hybrid solution approaches alone. The emerging ML approaches should also receive an equal amount of focus given to the rest of the existing solution approaches. Furthermore, more online and communication tools should be incorporated and translated into the dynamic and stochastic elements of RVRP. These online tools serve as a source for dynamic and stochastic elements that are obtained from the location of disasters in depicting the real situation of the affected locations. Finally, evacuation and rescue routing problems both deserve an equal amount of attention among the academicians, if not more. The huge gap between these problems and the delivery routing problems being addressed shows a disconnection between the theoretical and practical worlds.