2015 Informs Annual Meeting

TC23

INFORMS Philadelphia – 2015

TC21 21-Franklin 11, Marriott Innovations in Healthcare Operations Sponsor: Health Applications Sponsored Session Chair: Mili Mehrotra, University of Minnesota, 321 19th ave south, minneapolis, United States of America, milim@umn.edu 1 - Incentizing Less-Than-Fully-Qualified Providers for Early Diagnosis of Tuberculosis in India Sarang Deo, Assistant Professor, Indian School of Business Hyderabad, ISB Hyderabad, Gachibowli, Hyderabad, TS, 500032, India, sarang_deo@isb.edu, Milind Sohoni, Neha Jha A major driver of TB epidemic in India is delay in diagnosis by less-than-fully- qualified providers (LTFQs), who are typically the first point of contact for patients. This work is motivated by pilots funded by international donors to provide monetary incentives to LTFQs to induce earlier diagnosis. We develop a game-theoretic model to design an incentive contract that should be offered to LTFQs and calibrate it using realistic parameter estimates obtained from primary and secondary data. 2 - Optimizing Spatiotemporal Antiviral Release Schedules in a Pandemic Influenza Bismark Singh, Assistant Professor, University of Texas at Austin, Austin, TX, United States of America, ned@austin.utexas.edu, Nedialko Dimitrov To help the state of Texas plan influenza pandemic interventions, we build a stochastic MIP to compute time-based antiviral releases. We derive scenarios for the stochastic program from an epidemic simulator that accounts for the large amount of uncertainty in disease progression. We study the hardness of this problem, and present models and methods to solve it, even though a direct-solve is intractable because of the large number of scenarios. 3 - Online Scheduling of Operating Rooms Chaitanya Bandi, Kellogg School of Management, Northwestern University, Evanston, IL, United States of America, c-bandi@kellogg.northwestern.edu, Diwakar Gupta We consider the online operating room scheduling problem where we do not know the sequence of requests and associated surgery lengths beforehand. Given the uncertainty and the objective of feasible schedules, we model the uncertainty using a Robust Optimization (RO) approach, and utilize a RO framework to develop an interval-classification scheduling algorithm optimized under the RO framework. We obtain provable lower bounds on the performance and show promising results based on real data. 4 - Is Technology Eating Nurses? – Staffing Decisions in Nursing Homes Feng Lu, Assistant Professor, Purdue University, 403 W State St, TC22 22-Franklin 12, Marriott Analysis and Control of Queues Sponsor: Applied Probability Sponsored Session Chair: Hayriye Ayhan, Georgia Tech, Atlanta, GA, United States of America, hayriye.ayhan@isye.gatech.edu 1 - Control of Multiserver Energy-aware Queueing Systems Vincent Maccio, McMaster University, 1280 Main Street West, Hamilton, Canada, macciov@mcmaster.ca, Douglas Down We study the problem of controlling a multiple server system, where servers may be turned on or off. The cost function of interest is a combination of holding costs and energy costs (and potentially switching costs). We provide several structural results on the optimal policy - these structural results are enough to allow for the derivation of the optimal policy for a wide range of systems. Finally, we discuss how these policies compare with those extant in the literature. 2 - The Snowball Effect of Customer Slowdown in Critical Many-server Systems Jori Selen, PhD Candidate, Eindhoven University of Technology, De Zaale, Eindhoven, Netherlands, j.selen@tue.nl, Johan Van Leeuwaarden, Vidyadhar Kulkarni, Ivo Adan Customer slowdown describes the phenomenon that a customer’s service requirement increases with experienced delay. In healthcare settings, there is West Lafayette, IN, 47907, United States of America, lu428@purdue.edu, Huaxia Rui, Abraham Seidmann We study the effect of IT-enabled automation on staffing decisions in healthcare facilities using a unique nursing home IT data from 2006 to 2012. We also develop a strategic staffing model that incorporates technology adoption.

substantial empirical evidence for slowdown, particularly when a patient’s delay exceeds a certain threshold. For such threshold slowdown situations, we design and analyze a many-server system that leads to a two-dimensional Markov process. Analysis of this system leads to insights into the potentially detrimental effects of slowdown. 3 - Maximizing throughput in Non-collaborative Networks of Queues Tugce Isik, Georgia Institute of Technology, 755 Ferst Drive NW, Atlanta, GA, 30332-0205, United States of America, tugceisik@gatech.edu, Hayriye Ayhan, Sigrun Andradottir We study queueing networks with flexible non-collaborative servers. We introduce a processor sharing (PS) scheme that yields maximal throughput when buffers are infinite. For systems where the servers cannot work together at a station, we develop non-collaborative round-robin policies that approximate PS as the rotation of the servers becomes more frequent. We evaluate the performance of these policies in queueing networks with tandem, merge, and split topologies for different buffer sizes. 4 - Optimal Assignment of Authentication Servers to Different Customer Classes Daniel Silva, Georgia Tech, 755 Ferst Drive, Atlanta, GA, United States of America, dfsi3@gatech.edu, Hayriye Ayhan, Bo Zhang Consider a system where user requests for authentication arrive from several classes of customers, following independent Poisson processes. Each arrival has a class-dependent probability of being an impostor. The system has several authentication methods; each one has a known service time distribution, and a Type I and II error probability. A controller assigns a method to each user request. We model the system as a queueing network and find the structure of a cost- optimal routing policy. TC23 23-Franklin 13, Marriott Stochastic Modeling and Control of Production Systems Cluster: Stochastic Models: Theory and Applications Invited Session Chair: Sanket Bhat, McGill University, 1001 Sherbrooke Street West, Room 520, Montreal, QC, H3A 1G5, Canada, sanket.bhat@mcgill.ca 1 - Using an Artificial Neural Network Model and Approximate Dynamic Programming for Stochastic Control Han Wu, Student, University of Louisville, 2301 S 3rd St, Development of efficient control policies for dynamic production systems is difficult. The uncertain demands and large set up times on machines can cause significant problems. Consider an assembly line for dishwashers which require multiple types of wire racks that must be fabricated and coated at different machines. An Artificial Neural Network model is embedded within an approximate dynamic programming algorithm to search for a better production and inventory control policy. 2 - Resource Allocation Policies to Provide Differentiated Service Levels to Customers Louisville, KY, 40218, United States of America, han.wu@louisville.edu, Gerald Evans, Kihwan Bae We analyze resource allocation decisions for component manufacturers who supply components to several original equipment manufacturers (OEMs). OEMs differ in their demand variability and service level expectations. We derive policies that provide differentiated service to OEMs depending on their demand variability. Under the dynamic programming framework, we investigate the value of these policies to component manufacturers. 3 - A Newsvendor Problem with Price-sensitive and Uncertain Supply Z. Melis Teksan, University of Florida, ISE Dept. 303 Weil Hall, P.O. Box 116595, Gainesville, FL, 32611, United States of America, zmteksan@gmail.com, Meltem Tutar, Joseph Geunes We study a newsvendor problem in which the supply quantity depends on the price offered by the newsvendor to suppliers. We analyze the optimal ordering policy, which depends on the economics of overage and underage costs, as well as the relationship between price and supply quantity. We characterize the optimal supply-pricing policies for cases in which suppliers are also unreliable, i.e., supply capacity is both price-dependent and random. Ananth Krishnamurthy, Associate Professor, University of Wisconsin-Madison, 1513 University Avenue,, ME 3258, Madison, WI, 53706, United States of America, akrishn2@wisc.edu, Sanket Bhat

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