## 4 A General Solving Methodology
This characteristic becomes highly relevant when dealing with transportation problems, which are usually NP-hard; and (ii) the stochastic/fuzzy simulation component provides suitable tools to cope with different types of uncertainty, in order to provide high-quality solutions in terms of expected costs, expected profits, or risk/reliability indicators. A set of numerical instances of two well-known transportation topics--the vehicle routing problem (VRP) and the team orienteering problem (TOP)--demonstrates these advantages. The simultaneous consideration of stochastic and fuzzy uncertainty arises whenever a subset of elements in a transportation problem--e.g., customers, roads, or vehicles-allows us to model some uncertainty aspects using probability distributions, while others require fuzzy techniques due to their vagueness or to the lack of enough historical data. The well-known VRP and TOP have been useful in testing our approach. For the VRP, we have studied a numerical example in which demands associated with a group of customers are stochastic, while a different group of customers presents fuzzy demands. Regarding the TOP, we have analyzed a case study in which travel times between customers are stochastic for a group of edges, and fuzzy for another group. The obtained results show that employing our approach leads to improve the solution quality--in terms of total cost for the VRP, and total collected reward for the TOP--when uncertainty is considered. All in all, these numerical examples illustrate the efficiency of the proposed methodology to solve transportation problems combining, at the same time, deterministic, stochastic, and fuzzy elements, something that has been rarely explored in the existing literature despite its relevance in real-life applications. The challenges for future work are huge, given the increasing necessity of providing agile and very good solutions to real-world transportation problems considering uncertainty. In this sense, richer versions of the VRP and the TOP can employ and adapt our fuzzy simheuristic approach. This can be done, for instance, by including decisions about inventory management, multiple depots, facility location, or time windows, as well as by incorporating external dynamic conditions that force us to constantly update the routing plans. Conceptualization, A.A.J. and J.P.; methodology, A.A.J., J.P., P.J.C. and R.D.T. ; validation, P.J.C., J.P. and R.D.T. ; formal analysis, R.D.T., E.P.-B. and L.d.C.M. ; writing--original draft preparation, R.D.T., L.d.C.M. and E.P.-B. ; writing--review and editing, A.A.J., R.D.T. and E.P.-B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This work has been partially supported by the Spanish Ministry of Science (PID2019-111100 RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ program (2019-I-ES01-KA103-062602). The authors declare no conflict of interest. ## References

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