## 4 A Review of the Simheuristics Concept and Recent Applications  
Simheuristics is a simulation-optimization approach that combines the strengths of simulation and metaheuristic optimization algorithms to address complex real-world problems [23]. Several simulation-optimization approaches have been defined in the literature [24]. This approach is especially efficient in handling problems that have random elements. Simulation is adept at analyzing systems under stochastic uncertainty, but it has no intrinsic mechanisms to optimize the system and limitations for considering a broad range of scenarios. Optimization methods can search for efficient or optimal solutions, but they require a valid objective function for their operation. The term'simheuristics' refers to the integration of simulation in metaheuristic algorithms. By combining both techniques, simheuristics provides a robust approach that can tackle a range of diverse problems. It has gained significant attention in recent years due to its capability to solve complex problems across various domains such as transportation, logistics and urban delivery, supply chains, and other combinatorial problems [8]. The approach is based on the idea that the solution of a deterministic optimization problem can serve as the foundation for the original stochastic problem. A typical simheuristic algorithm employs a simulation component in a metaheuristic framework, which allows the metaheuristic to drive the search process in the solution space, while the simulation component manages the random elements of the stochastic optimization problem. The L&T sector was the initial area where the simheuristic methodology was developed. [PERSON] and [PERSON] [25] presented an efficient combination of optimization-simulation techniques using metaheuristics as a core element for decision support systems to solve many L&T problems. Also, [PERSON] et al. [23] used simheuristics for the optimization of reconfigurable production systems. Regarding sustainable mobility, simheuristics has made contributions to the prompt resolution of routing problems for electric vehicles with limited driving ranges and stochastic travel times. [PERSON] et al. [26] extended the use of simheuristics to resolve evacuation problems during extreme weather disasters, demonstrating a useful application of optimization-simulation techniques to people evacuation policies. In addition, simheuristics has been used to improve waste collection services in smart cities, as shown in the work by [PERSON] et al. [27], who implemented a simheuristic procedure to solve waste collection problems in Sydney (Australia). Likewise, [PERSON] et al. [28] analyzed the management of multimodal transportation networks for long-haul merchandise delivery, considering uncertainties in transportation costs and time. These applications can be supplemented with agile optimization of unmanned aerial vehicle routes for various logistic purposes, which reveals a promising future for simheuristics in modeling and optimizing urban and interurban mobility. Recently, [PERSON] et al. [29] combined simheuristics with constraint programming to optimize airport apron operations under uncertain conditions. Similarly, [PERSON] and [PERSON] [30] combined simheuristics with machine learning models to address the facility location and queuing problem in emergency scenarios where a large number of citizens require medical treatment. Simheuristics has also been combined with evolutionary algorithms to solve manufacturing problems [31; 32]. Likewise, it has been used to solve stochastic versions of well-known optimization problems [28; 33]. All in all, simheuristic algorithms offer various benefits, such as the capacity to resolve a diverse range of problems with minimal customization [34; 35; 36]. This property arises from the usage of a general-purpose simulation schema that can be adjusted to suit any problem domain [37; 38; 39].