2016 INFORMS Annual Meeting Program

SD35

INFORMS Nashville – 2016

SD33 203B-MCC Recent Advances in Simulation Sponsored: Simulation Sponsored Session Chair: Jing Dong, Northwestern University, Evanston, IL, United States, jing.dong@northwestern.edu Co-Chair: Jose Blanchet, Columbia University, New York, NY, Huajie Qian, University of Michigan, 2302 St Francis Drive, Apt B118, Ann Arbor, MI, 48104, United States, hqian@umich.edu Henry Lam We present a general framework to calibrate the statistical distance dictating the size of the uncertainty sets for distributionally robust optimization used in stochastic or simulation optimizations under uncertainty. We discuss the implications on the statistical guarantees of the resulting objective values and feasibility. We also compare these guarantees to sample average approximation. 2 - Multi-resolution Gaussian Markov Random Fields For Discrete Optimization Via Simulation Eunhye Song, Northwestern University, Evanston, IL, United States, EunhyeSong2016@u.northwestern.edu Barry L Nelson, Jeremy C Staum The Gaussian Markov Improvement Algorithm (GMIA), an optimization via simulation algorithm based on Gaussian Markov random fields (GMRF), has computational advantages in solving problems on a large discrete solution space. We extend GMIA to a multiresolution algorithm (MR-GMIA) to solve even larger problems. The solution space is divided into regions; each region becomes a “solution” in a region-level GMRF while solutions within each region are represented by a solution-level GMRF. Using complete expected improvement, MR-GMIA guides the search toward promising regions and promising solutions within the selected regions with global inference about the optimality gap for termination. 3 - Unbiased Monte Carlo Computations For Optimization And Functions Of Expectations Yanan Pei, Columbia University, yp2342@columbia.edu, Jose Blanchet, Peter W Glynn We present general principle for the design and analysis of unbiased Monte Carlo estimators for quantities such as functions of expectations. Our estimators possess finite work-normalized variance under mild regularity conditions. We apply our estimator to various settings of interest, such as optimal value estimation in the context of Sample Average Approximations, unbiased estimators for particle filters and conditional expectations. 4 - Estimation In The Tail Of The Gaussian Copula Raghu Pasupathy, Purdue University, West Lafayette, IN, 47907, United States, pasupath@purdue.edu We present ecoNORTA for efficient constrained random vector generation within the Gaussian and NORTA contexts. We propose three importance-sampling estimators for such settings, the first of which actively exploits knowledge of the local structure of the feasible region around a dominating point to achieve bounded relative error. The second and third estimators, for use in settings where information about the constraint set is not readily available, do not exhibit bounded relative error but are shown to achieve a slightly weaker form of efficiency. Numerical results on various example problems show promise. 204-MCC Joint Session HAS/MSOM-HC: Models and Analytics in Healthcare Operations Sponsored: Manufacturing & Service Oper Mgmt, Healthcare Operations Sponsored Session Chair: Joel Goh, Harvard Business School, Boston, MA, United States, jgoh@hbs.edu 1 - Accurate Prediction Of Case Duration Amirhossein Meisami, University of Michigan, Ann Arbor, MI, United States, meisami@umich.edu, Nick Kastango, Christopher Thomas Borum Stromblad, Mark P Van Oyen The primary goal of this study is to analyze the abundant data available prior to surgery and leverage this information to produce accurate case length predictions via novel statistical learning methodologies. We will be working with a rich database from Memorial Sloan Kettering Cancer Center to identify the essential SD34 United States, jose.blanchet@gmail.com 1 - On Calibrating Statistical Distances

features in defining case duration variability. The research also focuses on reducing the uncertainties and variations imposed by rare events that may arise in various procedures during a case. 3 - Admission Of Long Stay Patients In A Busy Pediatric ICU Fernanda Bravo, Assistant Professor, UCLA Anderson School of Management, Los Angeles, CA, United States, fernanda.bravo@anderson.ucla.edu, Michael McManus This work studies admission policies for complex patients in the ICU of a large pediatric academic hospital. There are four different patient types: medical, emergency, surgical, and transfers. Within these, long-stay-patients use a large amount of resources and limit the access to the unit. The ICU must always remain available for emergencies before accommodating elective admissions. As a result, many children are queued for complex surgeries and medical workups. We study policies to decide when to admit an long-stay-patient depending on the current ICU status, and future patients’ arrivals. 4 - Scheduling Work In Radiology Maria R. Ibanez, Harvard Business School, mibanez@hbs.edu Using detailed data on millions of radiological studies interpreted by physicians, we study the drivers of speed and quality of the interpretation, and identify implications for scheduling and allocation of work across workers. Service Operations Sponsored Session Chair: Laurens Debo, Dartmouth College, Tuck School of Business, Hanover, NH, 03755, United States, Laurens.G.Debo@tuck.dartmouth.edu Co-Chair: Luyi Yang, University of Chicago, Booth School of Business, Chicago, IL, 60637, United States, luyi.yang@chicagobooth.edu 1 - Queueing With Strategic Balking And System Design Yichen Tu, University of North Carolina, Chapel Hill, NC, United States, yichen1@live.unc.edu, Nur Sunar, Serhan Ziya We analyze a queueing system where customers decide whether to join or balk the system depending on the expected benefit they receive by joining a system. In this setting, we characterize the optimal choice of system design from the perspective of a social welfare optimizer. We also conduct numerical studies to shed light on the benefit of different system design choices. 2 - Risk/return Trade-off In Queues With A Nonlinear Waiting Cost Function Hossein Abouee-Mehrizi, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, haboueemehrizi@uwaterloo.ca, Ata G Zare, Renata Konrad We consider an M/M/1 queueing system and assume that each customer receives a value by getting served and suffers from a waiting cost. To analyze customers’ behavior, we consider the risk/return trade-off and a nonlinear waiting cost function. Customers are impatient and have a mixed attitude with respect to the risk. Before reaching a certain point in time, customers are risk-seeking, but after that they become risk-averse. We assume that customers follow a joint balking and abandonment strategy. We fully characterize the equilibrium joint balking and abandonment strategy and show that three types of equilibria may exist: global, myopic, and farsighted. 3 - Optimal Information Disclosure In M/M/1 Queues Shiliang Cui, Georgetown University, 548 Rafik B. Hariri Building, 37th & O Streets, NW, Washington, DC, 20057, United States, shiliang.cui@georgetown.edu, Jinting Wang Queue length is a very important parameter for customers to make a joining decision or not. We study optimal information disclosure policies in M/M/1 queues. 4 - Want Priority Access? Refer Your Friend To Move Up In Line Luyi Yang, University of Chicago, lyang6@chicagobooth.edu Laurens G Debo This paper studies the referral priority program, an emerging business practice adopted by a growing number of technology companies that manage a waitlist of customers. The program enables existing customers on the waitlist to gain priority access if they successfully bring in new customers. We find that the effectiveness of this novel mechanism as a marketing tool for customer acquisition and an operational approach for waitlist management depends crucially on the base arrival rate of the system. Referrals may not be generated when the base arrival rate is either too high or too low. Even when customer refer, the program could backfire (i.e., reduces the system throughput and customer welfare). SD35 205A-MCC Strategic Queueing Sponsored: Manufacturing & Service Oper Mgmt,

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