Informed decision making in production and logistics has not only to consider optimization issues but also uncertainty conditions that are often present in real-life applications. In this context, finding the solution that provides the optimal expected value for a cost or benefit objective function is usually not sufficient, since the variability or risk associated with the proposed solution needs to be measured as well. Accordingly, simulation-optimization methods are becoming a relevant tool when dealing with complex logistics and production systems. Among these methods, this paper focuses on simheuristics, which have showed their effectiveness in solving large-scale stochastic optimization problems. We discuss the main principles behind the simheuristics concept, compare this methodology with other similar ones in the simulation-optimization domain, and provide some insights on their use.