A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms

The advent of Industry 4.0 technologies and in particular the Cyber-Physical Systems, Digital Twins and pervasive connected sensors is transforming many industries, among which smart scheduling is one of the most relevant. This paper contributes to the research on scheduling by proposing a framework to include equipment health predictions into the scheduling activity and embedding a field-synchronized Equipment Health Indicator module into the DT simulation. The metaheuristic approach to scheduling optimization is performed by a genetic algorithm, that is connected with the DT simulator and provides various generations of scheduling alternatives that are assessed through the simulator itself. The paper also proposes a practical Proof-of-Concept of the innovative framework, by developing an architecture to identify how the various framework modules are implemented and by applying the framework to a real application case, set in a laboratory assembly line environment.