Informs Annual Meeting 2017

SB07

INFORMS Houston – 2017

SB07

2 - Modelling Surge Across Emergency Department, Operating Room, and Inpatient Beds with Generic, Data-driven, Discrete Event Simulation Model Carolyn Busby, University of Toronto, Toronto, ON, L6J.2W5, Canada, carolyn.busby@mail.utoronto.ca, Michael W. Carter Canadian hospitals routinely operate, at, near or over capacity. To manage beds, and serve patients when full, hospitals have written surge protocols that outline what actions should be taken to alleviate the congestion. However, although these protocols are routinely employed, they have not been fully captured in hospital models. Additionally, as the hospital fills up, capturing the interaction of operating rooms, the emergency department, and inpatient beds become critical. A data-driven, generic, discrete-event-simulation model is presented that captures these interaction and the process changes that occur when in surge. 3 - Coordinated Response of Hospitals in Disaster Bahar ShahVerdi, George Mason University, Washington, DC, United States, bshahver@masonlive.gmu.edu, Elise Miller-Hooks, Marcedeh TariVerdi A discrete event simulation framework is presented for assessing the benefits of coordinated response of hospitals in a disaster incident involving area-wide damage and mass casualties. Impacts on critical resources, physical spaces and demand are modeled. Findings from numerical experiments show the potential of joint capacity modification strategies. 330A Mobile and Operations in the Digital Economy Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: Jose A Guajardo, University of California-Berkeley, Berkeley, CA, 94720-1900, United States, jguajardo@berkeley.edu Co-Chair: Vibhanshu Abhishek, Carnegie Mellon University, Pittsburgh, PA, 19104, United States, vibs@cmu.edu 1 - Business Models in the Sharing Economy: Manufacturing Durable Goods in the Presence of Peer-to-Peer Rental Markets Jose A.Guajardo, University of California-Berkeley, Haas School of Business, 545 Student Services Bldg, Berkeley, CA, 94720-1900, United States, jguajardo@berkeley.edu Business models that focus on providing access to assets rather than on transferring ownership of goods have become an important industry trend. Motivated by this trend, this research analyzes the interaction between a manufacturer of durable goods and a peer-to-peer marketplace, characterizing market outcomes under alternative market structures. 2 - Product Heterogeneity and Externality of Sponsored Search on Online Marketplaces Siddhartha Sharma, Carnegie Mellon University, 4800 Forbes Avenue, 3024 Hbh, Pittsburgh, PA, 19104, United States, na, Vibhanshu Abhishek, Param Vir Singh E-commerce marketplaces like Amazon, Ebay and Flipkart have gained significant prominence in the last decade. In this paper, we examine a new phenomenon used by these marketplaces called Product Listing Ads (PLA) which allow third party sellers to promote their listings via an ad. Using a large scale randomized field experiment we measure the impact of PLA on the marketplace and on competing advertisers. 3 - Motivating Effective Mobile App Adoption in Multi-channel Shopping: A Large Field Experiment Tianshu Sun, University of Southern California, 3670 Trousdale Parkway, Bridge Hall, BR.I.310B, Los Angeles, CA, 90089, United States, tianshus@marshall.usc.edu, Lanfei Shi, Siva Viswanathan, Elena Zheleva Using a randomized field experiment involving 250,000 customers from a large ecommerce platform, we investigate i) whether and how a firm can motivate customers to adopt mobile apps and ii) the causal effect of induced mobile app adoptions on customers’ purchase behaviors. We find that i) both providing information and monetary incentive can lead to significant increase in customers’ app adoption; ii) the effect of mobile app adoption varies greatly depending on how customers are motivated. Although providing monetary incentives may lead to larger increase in mobile app adoption, such induced adoption does not result in more purchases in the long run. In contrast, providing information leads to effective mobile adoption that sustainably increases customers’ purchases, and overall profits of the firm. We further examine customers’ multichannel behavior and explore how induced app adoptions affect customers’ purchases. SB09

322A Medical Decision Making Sponsored: Health Applications Sponsored Session Chair: Zlatana Dobrilova Nenova, University of Denver, Denver, CO, United States, Zlatana.Nenova@DU.edu Co-Chair: M. Reza Skandari, University of Chicago, 5454 South Shore Drive, 914, Chicago, IL, 60615, United States, skandari@uchicago.edu 1 - Preference-sensitive Management of Post-mammography Decisions in Breast Cancer Diagnosis Mehmet Ayvaci, University of Texas at Dallas, Richardson, TX, United States, Mehmet.Ayvaci@utdallas.edu, Oguzhan Alagoz, Mehmet Eren Ahsen, Elizabeth Burnside Decision models representing the clinical situations where treatment options entail a significant risk of morbidity or mortality should consider the variations in risk preferences of individuals. In this study, we develop a stochastic modeling framework that optimizes risk-sensitive diagnostic decisions after a mammography exam. Using a risk-sensitive finite-horizon MDP model, we provide medical/policy insights. We empirically validate our model and demonstrate that the current medical practice of breast biopsies resemble risk- seeking behavior. 2 - Optimal Fractionation in Radiotherapy with Two Modalities Sevnaz Nourollahi, University of Washington, 6322 5th Avenue NE, Lower Unit, Seattle, WA, 98115, United States, sevnaz@uw.edu, Minsun Kim, Archis Ghate We present a formulation and a solution method for the optimal fractionation problem with two modalities using the standard linear-quadratic modelof dose- response. Numerical experiments and sensitivity analyses will be described to provide insight into the resulting dosing decisions. 3 - Optimal Strategies for Monitoring and Prediction of Chronic Kidney Disease Demand-for-care Zlatana Dobrilova Nenova, University of Denver, 2720 Shady Avenue, Apt 2, Denver, CO, 15217, United States, znenova@katz.pitt.edu, Jennifer S.Shang Chronic disease monitoring strategies involve understanding patient’s overall health state. In this research, we design a framework for optimizing such strategies, which consists of three models: two parameter estimation models (Case-based Reasoning and Survival Analysis) and one optimization model (MDP). We showcase our framework using chronic kidney disease, which is selected due to its complexities, prevalence and cost of care. Furthermore, we demonstrate how the proposed MDP model can be used to predict medium-term appointments’ demand, which is important when making staff hiring and patient recruitment decisions. 4 - Optimal Decision Making in a Markov Model with Parameter Uncertainty: the Case of Chronic Kidney Disease Mohammad Reza Skandari, University of Chicago, 5454 South Shore Drive, 914, Chicago, IL, 60615, United States, skandari@uchicago.edu, Steven Shechter We investigate a Markov decision process whose unknown transition parameters are revealed partially through state observation. Decisions are made as the state evolves. We use the model to study the optimal time to start preparing a type of vascular access for chronic kidney disease patients who will need dialysis. 322B Simulation in Healthcare Sponsored: Health Applications Sponsored Session Chair: Michael W. Carter, University of Toronto, Toronto, ON, M5S 3G8, Canada, carter@mie.utoronto.ca 1 - Cost-effectiveness Analysis of Nalmefene to Treat Alcoholism Greg Zaric, London, ON, Canada, gzaric@ivey.ca, Estefania Ruiz Vargas, Richard Zur We developed a microsimulation model to estimate the cost-effectiveness of nalmefene plus psychosocial support in an alcohol dependent population with high or very high drinking risk levels. The model simulates a large cohort from age 17 to age 110 in 1-year time steps. In each time step individuals are classified as being in one of three drinking states Among current drinkers, changes in consumption are based on age, sex, and current drinking status. Transition rates were estimated using three large databases, and treatment efficacy was modeled based on clinical trial results. SB08

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