Informs Annual Meeting 2017

SC09

INFORMS Houston – 2017

2 - Optimizing Vaccine Distribution in Low and Middle Income Countries Yuwen Yang, University of Pittsburgh, Department of Industrial

3 - Drug Dispensing Data Driven Modeling of Anticholinergic Drug Prescription Behavior Nan Kong, Purdue University, 206 South Marting Jischke Drive, Biomedical Engineering, West Lafayette, IN, 47906-2032, United States, nkong@purdue.edu Anticholinergic drug overdose is common among older adults in the U.S. The overdose leads to increased cognitive impairment and contributes to higher care spending. We develop an individually based state-transition simulation to characterize prescription behavior with incorporation of the variations among patients and pharmacists. We test various semi-Markov models to fit the continuous prescription pattern based on drug dispensing data from a metropolitan area. The simulation can help us conduct research on assessing various prescription behavior interventions. 4 - On the Effect of Electronic Patient Portal on Primary Care Delivery Xiang Zhong, Univ. of Florida, 380 Weil Hall, Gainesville, FL, 32611, United States, xiang.zhong@ise.ufl.edu, Aditya M. Prakash Electronic visit (e-visit), which allows patients and primary care providers communicating through secure messages, has enabled virtual care delivery as an alternative to traditional office visits. To help identify the conditions that e-visits lead to improved care delivery efficiency and patient access, we modeled the dynamics of appointment backlog using a Markov Modulated Poisson Process (MMPP), and developed numerical methods for computing system performance metrics. The insights obtained from the models provide guidance to care providers who are engaged in facilitating e-visits to apprehend the influence of the novel care delivery channel on their established practices. 5 - A Simulation Optimisation on the Hierarchical Health Care Delivery System Patient Flow Based on Multi-fidelity Models Yunzhe Qiu, PhD Candidate, Olin Business School, Haidian District, University City, MO, 63130, United States, qiuyunzhe@wustl.edu, Jie Song, Zekun Lu The imbalanced development among different levels of healthcare facilities in China’s urban healthcare system has raised the uneven patient flow distribution. We develop a method integrating the simulation, the multi-objective optimization and the simulation budget allocation together to comprehensively improve the overall system performances by finding the approximate Pareto patient flow distribution in the hierarchical healthcare system. A case study based on the real data shows the recommended Pareto optimal patient flow distribution can improve the overall hierarchical system performances and our methodology is qualified as a quantitative decision tool for decision makers 330A MSOM Energy and Sustainability Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: Yangfang Zhou, Singapore Management University, Singapore, 178899, Singapore, helenzhou@smu.edu.sg 1 - Promoting Solar Panel Investments: Feed-in-tariff Versus Tax-rebate Policies Safak Yucel, Georgetown University, Georgetown University, 3700 O. St. NW, 523 Hariri Building, Washington, DC, 20057, United States, safak.yucel@georgetown.edu, Volodymyr O. Babich, Ruben Lobel We compare two policies governments use to promote investment in solar panels by households: feed-in tariff and tax rebate. The feed-in tariff provides a guaranteed stream of payments to the households. The tax-rebate policy reduces the initial investment cost. We investigate the main economic forces that tilt the government’s preference towards one of the policies. One such force is the strategic waiting by forward-looking households in the face of uncertainty and irreversible investments. We model uncertainties in prices and investment costs, the percentage of myopic households in the economy, the heterogeneity in generating efficiencies, and the environmental benefits. 2 - Dynamics of Capacity Investment in Renewable Energy Projects Nur Sunar, UNC, 1604 Village Crossing Drive, Chapel Hill, NC, 27517, United States, nur_sunar@kenan-flagler.unc.edu, John R.Birge Using a continuous time model, we analyze the dynamics of capacity investment in renewable power. In this context, we explicitly identify the optimal policy. We also present some numerical examples to explain the structure of the optimal strategy. SC09

Engineering, Pittsburgh, PA, 15261, United States, yuy49@pitt.edu, Jayant Rajgopal, Hoda Bidkhori

The WHO Expanded Programme on Immunization aims to provide universal access to a recommended suite of vaccines, with a special focus on underserved low and middle income countries. Despite significant economic, geographical and demographic differences, the structure of the vaccine distribution network is almost identical across all these countries, when there is no compelling reason for this to be so. We present a mixed integer programming formulation to optimize the cold chain network, along with the consideration of cold storage and transportation. We study the structure of the model and examine several options to improve its computational performance with data derived from the real world. 3 - Optimal Pooling, Batching and Pasteurizing of Donor Human Milk Donor human milk - collected and dispensed via milk banks - is the standard of care for NICU and outpatient infants whose mother’s own milk is not a viable option. It undergoes complex processes at milk banks. We formulate integer programs to optimize the daily milk processing decisions, including the pooling of milk from different donors to meet macronutrient requirements across different product types, and the batching of pooled milk for efficient pasteurization. The numerical study shows an increase of approximately 25% for the overall utility. 4 - Robust Inverse Optimization with Application to Dietary Recommendation Taewoo Lee, University of Houston, E209 Engineering Bldg 2, 4722 Calhoun Rd, Houston, TX, 77204-4008, United States, tlee20@central.uh.edu, Kimia Ghobadi, Houra Mahmoudzadeh, Daria Terekhov In this talk, we explore the robustification of inverse optimization. Our work is motivated by problems in which the observation of the solution is partial, noisy, or uncertain. We build an uncertainty set around the observation and derive an inverse model that finds a cost vector that protects against the worst case scenario in the given uncertainty set. Our model generalizes previous work on single- observation inverse models. It can also be seen as more general than inverse optimization with multiple points, since the points can be thought of as a sample from the uncertainty set. 322B Stochastic and Simulation Modeling in Healthcare Management Sponsored: Health Applications Sponsored Session Chair: Xiang Zhong, Univ. of Florida, 380 Weil Hall, Gainesville, FL, 32611, United States, xiang.zhong@ise.ufl.edu 1 - Modeling of Cost-effective Interventions to Reduce Total Joint Replacement Read Missions Hyo Kyung Lee, University of Wisconsin-Madison, Madison, WI, United States, hlee555@wisc.edu, Rebecca Jin, Yuan Feng, Philip A. Bain, Jo Goffinet, Jingshan Li More than a million total joint replacements are performed in the US each year, where hip and knee replacements are the most common ones. Complications can occur in some replacements which lead to unplanned hospital readmissions. To reduce readmissions, prevalent studies focus on finding the risk factors without considering the intervention processes that are carried out post-discharge. Thus, we present a quantitative intervention process model that incorporates patients’ identified risk levels to provide patient-centered interventions. By investigating cost-effectiveness of various intervention policies on hospital readmissions, we seek to provide clinical guidelines. 2 - Achieving Optimal Patient-centered Interventions to Reduce Readmission for COPD Patients Sujee Lee, University of Wisconsin - Madison, Madison, WI, United States, slee776@wisc.edu, Philip Bain, Chris Baker, Jingshan Li Staring from 2014, the Centers for Medicare and Medicaid Services have penalized hospitals with high risk-adjusted, 30day unplanned readmission rates of the patients admitted for an acute exacerbation chronic obstructive pulmonary disease. In response, hospitals struggle to develop efficient interventions for COPD patient. To support decision making for the hospitals, this study introduces an optimization model to introduce an incentive based postdischarge intervention policy for COPD patients. Based on predicted risk of readmissions, optimal strategies to minimize readmission rate are presented to distribute incentive budget to encourage COPD patients complying with interventions. Ruichen Sun, University of Pittsburgh, 3700 O’Hara Street, 1048 Benedum Hall, Pittsburgh, PA, 15261, United States, rus19@pitt.edu, Lisa M.Maillart, Andrew J.Schaefer SC08

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