## 1 Introduction  
Analytical methods are commonly used by L&T managers to make informed decisions [1]. Optimization models are widely employed in various industries, including L&T, because of their ability to provide optimal solutions for complex challenges. When the inputs and constraints of an optimization problem are known, deterministic models can be solved using exact methods as long as they satisfy some mathematical properties [2]. However, in real-life L&T activities, there are often many variables and constraints, leading to NP-hard optimization problems, which might limit the effectiveness of exact methods, especially in large-size instances [3]. As a result, heuristic and metaheuristic approaches have been developed to provide near-optimal solutions to such problems in reasonable computing times [4]. In addition, in the context of L&T operations, the inputs may be subject to the uncertainty of parameters such as travel times, customer demands, service times, or battery durability in the case of electric vehicles. When these inputs can be modeled using random variables, simheuristic algorithms--which combine heuristics with simulation--can be used to address the corresponding stochastic optimization problem [5]. The term'simheuristics' or'sim-heuristics' was introduced in the last decade. Since then, the number of published articles using it has been continuously increasing. Figure 1 depicts this evolution until 2023 (where values with the ''*' symbol represent estimates). A noticeable increase has been encountered during the last years, when real-world problems triggered considering uncertainty in the search for solutions [6,7]. Hence, simheuristics has become a first-resource tool for dealing with NP-hard optimization problems under stochastic uncertainty [8]. However, stochastic uncertainty is not the only type of uncertainty found in real-world problems. For non-probabilistic uncertainty, fuzzy techniques can be used to model uncertainty [9]. This non-probabilistic uncertainty might be due to a lack or scarcity of data, which makes it difficult to model it using a theoretical probability distribution. Non-probabilistic uncertainty might also be associated with scenarios in which qualitative expert opinions must be considered. In order to illustrate the combination of two types of uncertainty conditions, [PERSON] et al. [10] propose a fuzzy simheuristics method, which hybridizes a metaheuristic with simulation and fuzzy logic. As explained by these authors, some inputs can be modeled using random variables, while others follow a fuzzy pattern. A fuzzy system based on fuzzy logic computes fuzzy outputs based on a set of rules established by a human expert, allowing for non-binary logic scenarios, where outputs have a partial degree of being 'true' or 'false'. Accordingly, the main contribution of this paper is to provide both conceptual and practical insights on how these 'fuzzy simheuristics' can be applied in the optimization of different L&T systems, including the well-known vehicle routing problem (VRP) [11], the team orienteering problem (TOP) [12], the permutation flow-shop problem (PFSP) [13,14], the location routing problem (LRP) [15], and the time-capacitated arc routing problem (TCARP) [16] under uncertainty conditions. This novel concept of fuzzy simheuristics has not been widely addressed in the literature so far. In order to close this gap, this paper reviews existing work on the topic, discusses the main concepts, and provides a novel cross-problem analysis of computational results. The remaining sections of this paper are organized as follows: Section 2 reviews the impact of uncertainty in logistics and transportation activities, while Section 3 discusses some related optimization problems. Section 4 provides a review of simheuristics. Section 5 explains how simheuristics can be naturally extended to fuzzy simheuristics. Section 6 provides an overview of recent applications of this novel fuzzy simheuristic methodology.