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DateTimeRoomSpeakerAffiliationPaper
September 209:30 AM3325 Graigner HallChris RyanBooth School, University of Chicago

Incentive Design for Operations-Marketing Multitasking

October 299:30 AM3560 Grainger HallKostas NikolopoulosBangor University

Looking for the Needle in the Haystack:
Evidence of the Superforecasting Hypothesis When Time and Samples are Limited


OIM Research Workshop
December 61:00 PM4580 Grainger HallAtalay AtasuScheller College of Business, Georgia TechJan Van MieghemKellogg School, Northwestern UniversityDual Sourcing and Smoothing Under Non-Stationary Demand Time Series: Re-shoring with SpeedFactories
December 62:30 PM

Leasing, Modularity, and the Circular Economy

December 6-74580 Grainger HallRyan BuellHarvard Business School, Harvard UniversitySurfacing the Submerged State: Operational Transparency Increases Trust in and Engagement with Government
December 79:00 AM4580 Grainger HallJan Van MieghemKellogg School, Northwestern UniversityDual Sourcing and Smoothing Under Non-Stationary Demand Time Series: Re-shoring with SpeedFactoriesAtalay AtasuScheller College of Business, Georgia Tech

Leasing, Modularity, and the Circular Economy


February 8March 159:30 AMBeril ToktayScheller College of Business, Georgia Tech3070 Grainger HallTinglong DaiCarey Business School, Johns Hopkins UniversityToo Much? Too Little? Economic Modeling of Physician Testing DecisionsTBD
April 59:30 AM3070 Grainger HallKumar RajaramAnderson 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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Leasing, Modularity, and the Circular Economy

Profile

Prof. Ataly Atalay Atasu, Professor, Scheller College of Business, Georgia Tech

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About ImageProf. Jan Van Mieghem, Professor, Kellogg School of Management, Northwestern University

We investigate the emerging trend of near-shoring a small part of the global production back to local SpeedFactories. The short lead time of the responsive SpeedFactory reduces the risk of making large volumes in advance, yet it does not involve a complete re-shoring of demand. Using a breakeven analysis we investigate the lead time, demand, and cost characteristics that make dual sourcing with a SpeedFactory desirable compared to off-shoring to a single supplier. We propose order rules that extend the celebrated inventory optimal order-up-to replenishment policy to settings where capacity costs exist and demonstrate their excellent performance. We highlight the significant impact of autocorrelated and non-stationary demand series, which are prevalent in practice yet challenging to analyze, on the economic benefit of re-shoring. Methodologically, we adopt Z−transforms and present an exact analysis of several discrete-time linear inventory models. 






ProfileImage RemovedTinglong Dai, PhDImage AddedProf. Beril ToktayTinglong Dai, Professor, Scheller College of Business, Georgia TechAssociate Professor, Carey Business School, Johns Hopkins University

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Dai
Dai

Few issues in the healthcare ecosystem are more salient than the utilization of medical tests. By some estimates, up to 30% of medical-testing decisions are deemed inappropriate, which may entail either over- or under-testing. All too frequently, the public attention has centered on over-testing. By comparison, under-testing has received little media coverage, but frequently appears in the medical literature. In addition, contrary to popular belief, the US trails most OECD countries in terms of the utilization of medical tests.

In this talk, I discuss several recent modeling efforts aimed at understanding physician decision-making leading to over- and under-testing. These efforts, motivated by ophthalmology and interventional cardiology practices, reflect clinical, financial, and operational incentives. I will also highlight implications for policymakers and healthcare executives.

My talk will draw from three papers:

Tinglong Dai, Shubhranshu Singh. 2018. Conspicuous by Its Absence: Diagnostic Expert Testing under Uncertainty. Johns Hopkins University Working Paper.

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

Profile photo of Kumar RajaramImage ModifiedProf. 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.