Erwin Pesch, Jacek Blazewicz, Benjamin Moseley, Denis Trystram, Guochuan Zhang

• Machine scheduling traditionally is the study of the sequencing of tasks on a single or several parallel or dedicated machines, with possibly different characteristics, subject to a set of constraints. Constraints commonly capture the limited availability of resources, reflect precedence relations between tasks or generally express some restrictions of processing tasks over time. Beyond satisfaction of the constraints, there is generally the goal to optimize an objective as a criterion of the quality of the solution delivered by some search method. There is a vast literature on this area of study, and it has led to numerous sophisticated heuristics, approximate and exact algorithms. Many of these problems are computationally intractable, and problems have been classified by their varying degrees of complexity. The motivation of many problems has come from applications in staff rostering and personnel planning, scheduling in parallel and distributed systems and production planning (see Blazewicz et al. (<a aria-controls="popup-references" aria-expanded="false" role="button" title="View reference" href="https://link.springer.com/article/10.1007/s10951-018-0571-3#CR1">2018</a>)). Over recent years, researchers have started to study scheduling problems that are derived from new applications and settings. These applications include scheduling in decentralized systems and selfish organizations, in sea ports and automotive production plants. Energy-efficient processing, fast data processing and online scheduling are challenging applications. Some of these scheduling problems reflect real-life situations by including results on real industrial datasets. This listing is only a small sample of the many new applications and scheduling problems that researchers are studying. The selected papers not only offer valuable insights on different facets of this current trend, but they also pave the way for future developments. In addition to the challenges related to changing industrial requirements and market conditions, future research studies will need to address challenges posed by emerging technologies. Industry 4.0, also called the Internet of Things, is closely tied with the digitalization of industrial processes and equipment, cyber-physical systems and the capability of real-time big-data processing.