## 1 Introduction  
Managers tend to rely on analytical methods that allow them to make informed decisions. This explains why optimization models play a key role in many industries and business, including the logistics and transportation sector. Whenever accurate information on the inputs and constraints of the optimization problem is available, the resulting deterministic models can be solved by using well-known methods, either of exact or approximate nature. Many optimization problems in real-life transportation involve taking into account a large number of variables and rich constraints, which often makes them to be NP-hard [1]. When this is the case, the computational complexity makes it difficult to obtain optimal solutions in a short computational time. At this point, heuristic approaches can provide near-optimal solutions that, in turn, cover all the requirements of the problem [2]. When dealing with challenging optimization problems, there is a tendency to divide them into sub-problems, which simplifies the difficulty but might also lead to sub-optimal solutions [3, 4]. Given the increase in computational power experienced during the last decade, and also the development of advanced metaheuristic algorithms, it is possible nowadays to solve rich and large-scale problems that were intractable in the past [5]. In the scientific literature on combinatorial optimization problems, it is often assumed that the input values are constant and known. However, in a real-world scenario this is rarely the case, since uncertainty is often present and affects these inputs. In the context of transportation and logistics, some examples of these inputs are: travel times, customer demands, service times, battery durability, etc. Whenever these inputs can be modeled by random variables, simheuristic algorithms--which combine heuristics with simulation--become a useful tool to address the associated optimization problem [6]. It should be noticed that simheuristics are designed to handle situations where uncertainty can be modeled by random variables, each of which follows a well-known probability distribution. When dealing with non-probabilistic uncertainty, fuzzy techniques might be a good choice. Therefore, fuzzy techniques can be particularly interesting for modeling uncertainty whenever it cannot be represented by random variables, for example: if not enough data are available, if the data cannot be fitted to a probability distribution, or if qualitative expert opinions must also be considered. [PERSON] et al. [7] illustrate with an example how to combine two types of uncertainty conditions using a fuzzy simheuristics, which hybridizes a metaheuristic with simulation and fuzzy logic. In their example, these authors assume that only some customer demands can be modeled by random variables, while others follow a fuzzy pattern. A fuzzy system is based on fuzzy logic. Inputs enter the system, which computes fuzzy outputs on the basis of a set of rules established by a human expert [7]. In order to obtain solutions that mix information from different sources, the output of the fuzzy system includes different degrees of membership for different groups. This means that a fuzzy system can handle decisions in a non-binary logic scenario, since the outputs have a partial degree of being 'true' or 'false'. Therefore, the main contribution of this paper is to provide both conceptual and practical insights on how fuzzy simheuristics can be applied in the optimization of different transportation systems, which include the well-known vehicle routing problem (VRP) under uncertainty conditions, as well as the team orienteering problem (TOP) under uncertainty conditions. A comprehensive introduction to both problems can be found in [PERSON] and [PERSON] [8] and [PERSON] et al. [9], respectively. Therefore, we address and discuss the novel concept of fuzzy simheuristics, which has hardly been addressed in the literature.