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| Date | Time | Room | Speaker | Affiliation | Paper |
|---|---|---|---|---|---|
| September 20 | 9:30 AM | 3325 Graigner Hall | Chris Ryan | Booth School, University of Chicago | |
| October 29 | 9:30 AM | 3560 Grainger Hall | Kostas Nikolopoulos | Bangor University | Looking for the Needle in the Haystack: |
| OIM Research Workshop | |||||
| December 6 | 1:00 PM | 4580 Grainger Hall | Jan Van Mieghem | Kellogg School, Northwestern University | Dual Sourcing and Smoothing Under Non-Stationary Demand Time Series: Re-shoring with SpeedFactories |
| December 6 | 2:30 PM | 4580 Grainger Hall | Ryan Buell | Harvard Business School, Harvard University | Surfacing the Submerged State: Operational Transparency Increases Trust in and Engagement with Government |
| December 7 | 9:00 AM | 4580 Grainger Hall | Atalay Atasu | Scheller College of Business, Georgia Tech | |
| February 8 | 9:30 AM | 3070 Grainger Hall | Tinglong Dai | Carey Business School, Johns Hopkins University | Too Much? Too Little? Economic Modeling of Physician Testing Decisions |
| April 5 | 9:30 AM | 3070 Grainger Hall | Kumar Rajaram | Anderson School, UCLATBD | Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application |
For more information please contact Prof. Bob Batt, bob.batt@wisc.edu.
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Tinglong Dai, Mustafa Akan, Sridhar Tayur. 2017. Imaging Room and Beyond: The Underlying Economics behind Physicians’ Test-Ordering Behavior in Outpatient Services. Manufacturing & Service Operations Management 19(1) 99–113.
Tinglong Dai, Xiaofang Wang, Chao-Wei Hwang. 2018. Clinical Ambiguity and Conflicts of Interests in Interventional Cardiology Decision-Making. Johns Hopkins University Working Paper.
Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application
Prof. Kumar Rajaram, Professor, Anderson School of Management, UCLA
We consider the problem of minimizing daily expected resource usage and overtime costs across multiple parallel resources such as anesthesiologists and operating rooms, which are used to conduct a variety of surgical procedures at large multispecialty hospitals. To address this problem, we develop a two-stage, mixed-integer stochastic dynamic programming model with recourse. The first stage allocates these resources across multiple surgeries with uncertain durations and prescribes the sequence of surgeries to these resources. The second stage determines actual start times to surgeries based on realized durations of preceding surgeries and assigns overtime to resources to ensure all surgeries are completed using the allocation and sequence determined in the first stage. We develop a data-driven robust optimization method that solves large-scale real-sized versions of this model close to optimality. We validate and implement this model as a decision support system at the UCLA Ronald Reagan Medical Center. This system effectively incorporates the flexibility in the resources and uncertainty in surgical durations, and explicitly trades off resource usage and overtime costs. This has increased the average daily utilization of the anesthesiologists by 3.5% and of the operating rooms by 3.8%. This has led to an average daily cost savings of around 7% or estimated to be $2.2 million on an annual basis. In addition, the insights based on this model have significantly influenced decision making at the operating services department at this hospital.