Modern industrial challenges require solutions that are both efficient and reactive. Heuristic-based approaches allow production systems to react quickly to unexpected events and disturbances. Thus, the term HyperHeuristic (HH) covers a wide variety of techniques that allow the selection or generation of heuristics. To conciliate the speed of heuristics with the necessary global performance, a number of mechanisms have been proposed in the literature. This paper first presents the HHs. Afterward, inspired from previous works, a classification is given to categorize them. In addition, an enhancement is proposed. Indeed, a third type of HHs is added (i.e. to classic selection HHs and generation HHs). This new category is named “mixed” HHs. Then, different contributions from the literature to dynamic scheduling are highlighted. Before concluding, a number of trends and future directions such as the use of Machine Learning and simulation are explored.