## 8 Conclusions  
In the context of T&L systems, the intersection of fuzzy systems, simulation, and metaheuristics has been explored. This combination provides powerful tools for solving complex optimization problems that involve both stochastic uncertainty and non-probabilistic uncertainty. The approach, known as 'fuzzy simheuristics', has shown great promise in addressing T&L-related problems such as the vehicle routing problem and the team orienteering problem. In particular, our numerical experiments have illustrated that traditional methods designed for deterministic versions of these problems provide sub-optimal solutions when both stochastic and fuzzy components are considered. Likewise, although it is logical to assume that solutions will decrease in quality (average values) as uncertainty levels increase, our methodology is able not only to show that effect for the different problems being considered but also to measure the impact on uncertainty in these solutions. As illustrated in the numerical analysis, the proposed approach can also provide lower and upper bounds to the optimal solution under uncertainty by simply considering the value of the best deterministic solution when it is evaluated in a deterministic (ideal) scenario as well as in a real scenario under uncertainty conditions. The advantages of fuzzy simheuristics lie in its ability to benefit from the strengths of each approach. The metaheuristics component provides the efficiency necessary to explore the solution space quickly and find near-optimal solutions in short computational times. The stochastic and fuzzy simulation components provide tools to cope with different types of uncertainty, providing high-quality solutions in terms of expected costs, expected profits, or risk and reliability indicators. Although fuzzy simheuristics is a relatively new approach, the results so far are promising. This paper has discussed a number of potential applications for different domains, including transportation planning and logistics optimization. However, there are many further areas where fuzzy simheuristics could be beneficially applied, such as mobility in smart cities, financial portfolio optimization, or energy system management. Of course, the proposed approach also has limitations that need to be considered. In particular, modeling real-life uncertainty with the proper random and fuzzy variables is not always an easy endeavor, or at least one that can be performed with perfect accuracy. These modeling errors can affect the quality of the final results. Also, depending on the specific problem being addressed, the computational times associated with a hybrid algorithm that combines metaheuristics, simulation, and fuzzy logic can be relatively high, specially for large-sized instances of complex problems. Special attention has to be paid to the simulation component, which could jeopardize the computational time of the metaheuristic one. Finally, one has to remember that the proposed methodology is an approximate algorithm. Hence, the optimality of the solutions is not guaranteed. Still, for these NP-hard and large-scale optimization problems under stochastic and fuzzy uncertainty, there is no methodology that can guarantee optimal solutions. Looking to the future, there are several lines for further research that could help to advance the field of fuzzy simheuristics. One promising direction is to explore the use of reinforcement learning techniques to improve the efficiency of the approach by employing surrogate models and considering dynamic environments. Another option is to investigate the use of other types of simulation, such as Monte Carlo simulation or time-discrete simulation based on agent-oriented modeling, to better analyze complex systems and their interactions. Finally, there is great potential for applying fuzzy simheuristics to real-world problems. Conceptualization, A.A.J. and M.A. ; methodology, J.P., D.P. and D.R. ; investigation, J.P. and A.A.J. ; writing--original draft preparation, all authors; writing--review and editing, A.A.J.