U-M IOE’s Xiuli Chao receives funding from Johnson & Johnson for a project centered on developing optimization algorithms to improve continuous production process.
Xiuli Chao, U-M Industrial and Operations Engineering (IOE) professor, has received funding from Johnson & Johnson for a project focused on developing algorithms for production allocation and sequencing using the Rhythm Wheel Concept.
“We are pleased that Johnson & Johnson funded this project on improving processing engineering,” Chao said. “It will allow us to develop efficient near-optimal algorithms for economic lot size scheduling through a Rhythm Wheel Concept, that will be valuable to any company having a continuous production process.”
“We are pleased that Johnson & Johnson funded this project on improving processing engineering. It will allow us to develop efficient near-optimal algorithms for economic lot size scheduling through a Rhythm Wheel Concept, that will be valuable to any company having a continuous production process.”
Xiuli Chao
Professor, U-M Industrial & Operations Engineering
The Rhythm Wheel Concept is a lean planning and scheduling tool that improves product planning and scheduling. This concept will allow the project to improve continuous production processes, such as pharmaceutical or refinery processes. The research will be applicable to any process engineering with multiple products.
Chao will serve as the co-principal investigator of this project. Ravi Anupindi, professor of Operations and Technology in the Ross School of Business, will serve as the principal investigator. Roman Kapuscinski, professor of manufacturing management in the Ross School of Business, and Sentao Miao, UM-IOE doctoral student, are also collaborating with Chao and Anupindi on the project.
Xiuli Chao joined U-M IOE in 2007. His research interests include queueing, scheduling, financial engineering, inventory control and supply chain management, and online optimization. Applications of his research include energy, manufacturing and service systems. His current research includes work on data-driven optimization of online retailing, and a project focused on improving the efficiencies of sharing economy through enhanced matching and contract design.
