Wei Xie

Assistant Professor
Wei Xie

Professor Xie’s research interests focus on computer simulation of stochastic systems, risk management, and data analytics. Applications include supply chains, financial engineering and production systems. On-going research includes: simulation optimization, risk management for supply chain systems and data-driven operations management.

Contact Information: 

Phone: (518)276-6622
Email: xiew3@rpi.edu
Web: http://homepages.rpi.edu/~xiew3/

Focus Area: 
computer simulation of stochastic systems, risk management, and data analytics

B.S., Mechanical Engineering, Yangtze University
M.S., Computational Mechanics, University of Nebraska-Lincoln
M.S., Industrial Engineering and Management Sciences, Northwestern University, 2009
Ph.D. Industrial Engineering and Management Sciences, Northwestern University, 2014

Sample Research Projects: 
  • Data-Driven Approach for Dynamic Operations Management in Global Supply Chains:Modern data collection techniques have resulted in availability of rich data streams forcomplex global supply chains, e.g., demand and supply data. Our study focuses onusing large-scale discrete-event stochastic simulation to support real-time decisionmaking. Specifically, we online update input processes that can correctly andefficiently account for the information extracted from data streams. Then, asimulation optimization approach is used to dynamically find optimal real-timedecisions. Our study could deliver reliable and cost efficient adaptive supply chainsystems.
  • Statistical Uncertainty Analysis for Stochastic Simulation: When we use simulation toevaluate the performance of a stochastic system, the simulation often contains inputmodels estimated from real-world data. There is both simulation and input uncertaintyin the system performance estimates. For the independent input data, weproposed rigorous approaches to quantify the impact of both input and simulationestimation uncertainty on system performance estimate: the metamodel-assistedbootstrapping approach and a Bayesian framework. Both approaches can makeeffective use of the simulation budget and provide good finite-sample performance.
Selected Publications: 
  1. Xie, Wei, Barry L. Nelson, Russell R. Barton (2014). A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation. Operations Research, Vol. 62, No. 6, pp. 1439-1452.
  2. Barton, Russell R., Barry L. Nelson, Wei Xie (2014). Quantifying Input Uncertainty via Simulation Confidence Intervals . INFORMS Journal on Computing, volume 26, Issue 1, pages: 74-87.
  3. Pei, Jinxiang and Diego Klabjan and Wei Xie (2013). Approximations to Auctions for Digital Goods with Share-averse Bidders. Electronic Commerce Research and Applications, volume 13, Issue 2, pages: 128-138.