Online Scheduling in Smart Manufacturing
Abstract
Empowered by data collection entities and computing entities, smart manufacturing systems
are much more advanced than traditional manufacturing systems in terms of information availability, systems reliability and productivity. Considered as the key to Industry 4.0, smart manufacturing also brings research questions such as how to improve information availability and how to utilize information in production planning, maintenance scheduling and other operational decisions. This dissertation research thus focuses on several optimization problems that are or envisioned to be prevalent in smart manufacturing systems, including joint production and maintenance decision making problem, polling system scheduling problem, and age of information-based scheduling problem. In solving these optimization problems, we provide policies and algorithms from both online scheduling and queueing control perspectives and develop advanced mathematical frameworks to evaluate the performance of these policies. While this research focuses on a small part of the vast smart manufacturing domain, its scope is wide enough to cover many important problems that exist in both physical and cyber layers of smart manufacturing systems. We expect our research to contribute significantly to the advancement of smart manufacturing, and our models and analysis to also contribute to development of online optimization, queueing theory and information theory. Our models and methodologies can also be adapted to improve system efficiency and reliability, in many other domains which are equipped with system intelligence.
Citation
Xu, Jin (2020). Online Scheduling in Smart Manufacturing. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /192361.