In recent years, there has been a growing number of natural computing techniques, including genetic algorithms, neural networks, cellular automata and (hyper/meta)-heuristic methods for a variety of different scheduling problems. The aim of this workshop is to bring together researchers and practitioners to share their experiences and report on emerging approaches in solving real-world scheduling problems. A particular interest will be on approaches that give a deeper insight into scheduling problem classes, and that enable the exploitation of structural information during the automated search for a solution to a given problem. General purpose approaches used for automated generation of heuristics for solving single and multi-objective scheduling problems and issues related to development of such approaches are also of particular interest. Data science techniques improving heuristic optimisation is a growing area of research. Such novel applications or reflections on particular line of work in scheduling are also of interest.