Pengyi Shi

Assistant Professor of Operations Management
Krannert School of Management
Purdue University
Email: shi178 AT purdue DOT edu

[CV]

    Pengyi Shi


About me

I am an Assistant Professor of Operations Management at the Krannert School of Management, Purdue University. I joined Krannert in Janurary 2014.

I obtained my Ph.D. degree from the School of Industrial and Systems Engineering at Georgia Institute of Technology in December 2013. I received my B.Eng. in Applied Mathematics and Mechanics and B.A. in Economics (double major) from Peking University, China, in June 2007. My Ph.D. advisors are Jim Dai and Pinar Keskinocak. Here is a copy of my Ph.D. thesis [pdf].

Research

My research focuses on building data-driven, high fidelity models and developing analytical methods to support decisions making under uncertainty in various healthcare systems.

Currently I am studying patient flow models for improving hospital operations and patient outcomes. In the past, I have worked on spatial-temporal epidemic models to study the course of influenza pandemic.

I strive to develop high impact research and tackle real-world problems. I have been collaborating with practitioners and faculty members from different healthcare organizations, and worked extensively with large-scale data sets from hospitals in the US, Singapore, and China.

Working Papers

denotes industry/medical collaborator; underline denotes student
  1. H. Bastani, P. Shi
    Proceed with Care: Integrating Predictive Analytics with Patient Decision-Making.
    Invited book chapter for Modeling for Health: Making Changes. [Draft]

  2. J. E. Helm, P. Shi, S.H. Heese, and A. Mitchell
    An Operational Framework for the Adoption and Integration of New Diagnostic Tests into Emergency Department Workflow.
    Major revision at Production and Operations Management. [Abstract and full paper]

  3. J. Dong, P. Shi, F. Zheng, and X. Jin
    Off-service Placement in Inpatient Ward Network: Resource Pooling versus Service Slowdown.
    Preparing resubmission to Manufacturing and Service Operations Management. [Abstract and full paper]
    • Selected to 2019 MSOM Conference Healthcare SIG

Papers Accepted and Published

denotes industry/medical collaborator; underline denotes student
  1. P. Shi, J. E. Helm, J. Deglise-Hawkinson, and J. Pan
    Timing it Right: Balancing Inpatient Congestion versus Readmission Risk at Discharge.
    Operations Research. Forthcoming. [Abstract and full paper]
    • First place, Pierskalla Best Paper Award, INFORMS HAS, 2018
    • Second place, College of Healthcare Operations Management Best Paper Award, POMS, 2019
    • Selected to 2019 CHOM Mini Conference Showcase Presentations
    • Selected to 2019 MSOM Conference Healthcare SIG

  2. J. G. Dai, P. Shi
    Recent Modeling and Analytical Advances in Hospital Inpatient Flow Management.
    Production and Operations Management. Forthcoming. [Abstract and full paper]
    • Invited paper for special issue on Frontier Analytic Modeling and Methods for OM.

  3. A. Alaeddini, J. E. Helm, P. Shi, and S. Faruqui
    An integrated framework for reducing hospital readmissions using risk trajectories characterization.
    IISE Transactions on Healthcare Systems Engineering. 2019; 9(2):172-185.
    • Former title: "A Prediction Model for Patient Readmission Risks with Kernel Principle Component Analysis."

  4. J. G. Dai, P. Shi
    Inpatient Bed Overflow: An Approximate Dynamic Programming Approach.
    Manufacturing and Service Operations Management. 2019; 21(4):894-911. [Abstract and full paper]

  5. J. Feng, P. Shi
    Steady-state Diffusion Approximations for Discrete-time Queue in Hospital Inpatient Flow Management.
    Naval Research Logistics. 2018; 65(1):26-65. [Abstract and full paper]

  6. J. G. Dai, P. Shi
    A Two-Time-Scale Approach to Time-Varying Queues for Hospital Inpatient Flow Management.
    Operations Research. 2017; 65(2):514-36. [Abstract and full paper]

  7. P. Shi, M. Chou, J. G. Dai, D. Ding, and J. Sim
    Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time.
    Management Science. 2016; 62(1):1-28. [Main Paper] [Online Supplement]
    • Former title: "Hospital Inpatient Operations: Mathematical Models and Managerial Insights."

  8. P. Shi, F. Dexter, R. H. Epstein
    Comparing Policies for Case Scheduling Within One Day of Surgery by Markov Chain Models.
    Anesthesia & Analgesia. 2016; 122(2):526-38.

  9. F. Dexter, P. Shi, R. H. Epstein
    Descriptive study of case scheduling and cancellations within one week of the day of surgery.
    Anesthesia & Analgesia. 2012; 115(5): 1188-95.

  10. P. Shi, P. Keskinocak, J. L. Swann, B. Y. Lee
    The Impact of Mass Gatherings and Holiday Travelling on the Course of an Influenza Pandemic: A Computational Model.
    BMC Public Health. 2010; 10: 778.

  11. P. Shi, P. Keskinocak, J. L. Swann, B. Y. Lee
    Modeling Seasonality and Viral Mutation to Predict the Course of an Influenza Pandemic.
    Epidemiology and Infection. 2010; pp. 1-10.

Other Papers

  • P. Shi, J. G. Dai, D. Ding, J. Ang, M. Chou, X. Jin and J. Sim
    Patient Flow from Emergency Department to Inpatient Wards: Empirical Observations from a Singaporean Hospital.
    This is a 69-page document on a comprehensive empirical study of inpatient flow management. [pdf]

Contact Information

403 W State St Kran 472
Krannert School of Management
Purdue University
West Lafayette, IN 47907
Office phone: (765) 494-0458
Email: shi178 at purdue.edu