Complex decision-making processes, especially those associated with NP-hard optimization problems, become even more challenging when faced with uncertainty and dynamic conditions. Traditional approaches like simulation and heuristics provide effective but limited solutions, often unable to adapt to changing environments. In this paper, we introduce sim-learnheuristics, a methodological tool combining simulation, metaheuristics, and machine learning. Sim-learnheuristics employ simulation models to model uncertainty, metaheuristic algorithms to efficiently explore the solution space, and machine learning methods to guide and adapt the heuristic process based on evolving conditions. We describe the methodology, provide pseudo-code, and discuss the main parts of an implementation that illustrates the adaptability of this approach. A review of related work is included, showing the potential of sim-learnheuristics to improve decision making in real-life scenarios under dynamic and stochastic conditions.