Prof. Xiuli Chao is the Ralph L. Disney Professor of Industrial and Operations Engineering at the University of Michigan, Ann Arbor. His research spans queueing theory, inventory control, supply chain management, and service operations management. He is the co-author of books on operation scheduling and queuing networks, and has extensive consulting experience in logistics, supply chains, and inventory management. In addition, he is an Amazon Scholar in Supply Chain Optimization Technology (SCOT).
Complementary research focuses
During his stay at TUM, Prof. Chao worked closely with Prof. Jingui Xie on research at the intersection of queueing theory and data-driven optimization in service systems, with a particular emphasis on their shared field of interest, healthcare applications.
With the growing availability of data in healthcare and other service systems, their joint work aimed to develop methodologies that systematically integrate empirical data into queueing theory to improve modeling, monitoring, and control of complex systems. “By combining data-driven insights with advanced analytical techniques, we aimed to improve customer demand estimation, better capture uncertainty in system dynamics, and develop effective, near-optimal strategies for resource allocation and operational decision-making”, explains Chao their concept.
Before inviting Prof. Chao to Germany, Prof. Xie and he had met repeatedly at international research conferences, where they discovered their shared commitment to improving healthcare systems worldwide. Jingui Xie is an Associate Professor of Business Analytics at the TUM School of Management at TUM Campus Heilbronn. Data availability and advancement in machine learning techniques make accurate predictions of the future a foreseeable reality. Prof. Xie’s aims to efficiently incorporate the predictive information into the decision-making through a joint estimation and optimization framework. In particular, his research leverages big data and analytics to enhance global healthcare operations.
… and now?
Six months have passed since the research stay of Prof. Chao at TUM, and the momentum has been remarkable: One paper has been completed and is currently under review with a journal, and another is under preparation, truly a success for the TUM Global Visiting Professor Program.
The published study develops a hierarchical optimization framework to determine population‑level antibiotic duration targets and the corresponding daily discontinuation policies, showing that optimal decisions follow a threshold structure and may justify prolonged therapy when constraints are loose. Using real ICU data, the authors demonstrate that the optimized policy reduces both treatment duration and failure rates while balancing therapeutic benefits against antimicrobial resistance costs.
TUM Global Visiting Professor Program
The TUM Global Visiting Professor Program promotes international academic exchange by enabling distinguished researchers from leading institutions worldwide to collaborate on-site with TUM researchers. The program aims to connect experts, to support knowledge transfer, joint research activities, and long-term international cooperation and exchange, strengthening TUM’s global research network and fostering interdisciplinary collaboration.
By bringing together experts across borders, the program creates opportunities for new insights, discoveries, and groundbreaking observations. It provides funding to MDSI Core Members who wish to invite international colleagues to TUM for short visits ranging from one week to three months.
Publication that resulted from the research visit and collaboration:
Qiu, Feier, and Chao, Xiuli, and Geng, Na and Xie, Jingui; Optimizing ICU Antibiotic Usage with Antimicrobial Resistance Considerations (December 01, 2025).
Available at SSRN: https://ssrn.com/abstract=5882442 or DOI:10.2139/ssrn.5882442
Further information on the TUM Global Visiting Professor Program:
TUM Global Visiting Professor Program - Funding opportunities for international visiting professors - TUM Global