Dr. Jingui Xie is associate professor of Business Analytics since 2020 at the TUM School of Management, TUM Campus Heilbronn. Data availability and advancement in machine learning techniques make accurate predictions of the future a foreseeable reality. Prof. Xie’s research aims to efficiently incorporate the predictive information into the decision making though a joint estimation and optimization framework. In particular, he is interested in using big data and analytics to improve healthcare worldwide.
Areas of interest
The Coronavirus (COVID-19) has become a severe public health problem globally. This project aims to determine whether the temperature, air pollution, social mobility etc. are essential factors in the infection caused by this novel coronavirus. The goal is to efficiently allocate limited medical resource even when the prediction with data and model is highly inaccurate.
J. Xie, Y. Zhu. Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of The Total Environment, 2020, p.138201.
The project is to develop a sequential and dynamic decision process with predictions to support decisions on medical treatment continuation and apply the model to the mechanical ventilator extubation problem in an intensive care unit (ICU). Using patient-level data, we compare the performance of different policies and demonstrate that incorporating predictive information can reduce ICU length-of-stay and decrease failure rate of ventilated patients, especially for patients with poor initial conditions.
Many real-world optimization problems have input parameters estimated from data whose inherent imprecision can lead to fragile solutions that may impede desired objectives and/or render constraints infeasible. We aim to propose a joint estimation and robustness optimization framework to mitigate estimation uncertainty in optimization problems by seamlessly incorporating both the parameter estimation procedure and the optimization problem.
Zhu, Taozeng and Xie, Jingui and Sim, Melvyn, Joint Estimation and Robustness Optimization (February 16, 2019). Available at SSRN: http://dx.doi.org/10.2139/ssrn.3335889
Find all available project studies under my chair here.
T. Zhu. J. Xie, M. Sim. Joint Estimation and Robustness Optimization. Forthcoming at Management Science, 2021.
J. Xie, G. G. Loke, M. Sim and L. S. Wei. The Analytics of Bed Shortages: Coherent Metric, Prediction and Optimization. Forthcoming at Operations Research, 2021.
J. Xie, W. Zhuang, M. Ang, M. Chou, L. Luo and D. D. Yao, Analytics for Hospital Resource Planning—Two Case Studies. Forthcoming at Production and Operations Management.
C. Girard, L. V. Green, M. E. Lewis and J. Xie. A Constrained Optimization Problem for a Two-Class Queueing Model. Forthcoming at Naval Research Logistics.
J. Liu, J. Xie, K. K. Yang and Z. Zheng. Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics. Manufacturing & Service Operations Management, 2019, 21(4): 780-797.
P. Cao, Y. Wang, J. Xie. Priority service pricing with heterogeneous customers: Impact of delay cost distribution. Production and Operations Management, 2019, 28(11): 2854-2876.
G. Zayas-Cabán, J. Xie, L.V. Green, M.E. Lewis. Dynamic control of a tandem system with abandonments. Queueing Systems, 2016, 84(3):279-293.
P. Cao, J. Xie. Optimal control of a multiclass queueing system when customers can change types. Queueing Systems, 2016, 82(3): 285-313.
P. Cao, J. Xie. Control of a stochastic inventory system with joint production and pricing decisions. IEEE transactions on Automatic Control, 2016, 61(12):4235 – 4240.
B. Huang, J. Xie, and Q.-M. He. Cyclic Change of Server’s Performance: Impacts and Applications. IEEE Transactions on Automatic Control, 2014, 59(3): 703-713.
Q.-M. He, J. Xie, X. Zhao, Priority queue with customer upgrades. Naval Research Logistics, 2012, 59: 362-375.
J. Xie, Q.-M. He, X. Zhao, On the Stationary Distribution of Queue Lengths in a Multi-Class Priority Queueing System with Customer Transfers. Queueing Systems, 2009, 62(3): 255-277.