In smart factories, production scheduling can be formulated for different types of problems that become more and more complicated. For example, in line with the trends toward mass-customization and customer-focused manufacturing, single-machine scheduling problems with due dates, job priorities, and dynamic arrivals frequently arise in order to meet the demands of customers in a timely manner. Also, to improve productivity and flexibility, smart factories might have one or more alternative machines for each process and the type of problem can be defined as a flexible job shop-scheduling problem.
Whereas some types of scheduling problems can be solved by mathematical models such as mixed-integer linear programming (MILP) to find optimal solutions to small-sized problems, they are not applicable to larger problems, due to the nature of NP-hard problems for which they are scaled up, and the computational time incurred consequently. Furthermore, much of the underlying logics of actual operations are implicit and challenging to capture intuitively, because manufacturing systems are often too complex for full understanding of all important attributes, due especially to the massive data from multiple sources and sensors that they have to handle. Thus, there is a significant need for research that can help systems make better decisions by extracting underlying decisions as dispatching rules while considering specific environments.