Informs Annual Meeting 2017

SD11

INFORMS Houston – 2017

SD11

computationally difficult since the resulting optimization problems are high- dimensional and non-convex. To this end, we develop a general approximation framework that provides the first efficient algorithms with rigorous approximation guarantees for a wide range of objective functions and controls. 2 - Pooling Queues with Discretionary Service Capacity Hummy Song, The Wharton School, University of Pennsylvania, 560 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA, 19104, United States, hummy@wharton.upenn.edu, Mor Armony, Guillaume Roels Contrary to traditional queuing theory, recent case studies in health care and call centers indicate that pooling queues may not necessarily result in less expected work-in-process. We develop a game-theoretic model that proposes that this may arise when servers have some discretion over their choice of service capacity and are work averse, i.e., bear a cost associated with either their expected workload or their degree of busyness. Our work suggests that system designers may need to consider the servers’ type and extent of work aversion as well as their degree of capacity choice discretion before pooling their workload. 3 - Matching while Learning Ramesh Johari, Stanford University, Huang Engineering Center Rm 311, 475 Via Ortega, Stanford, CA, 94305-4121, United States, ramesh.johari@stanford.edu We consider the problem faced by a service platform that needs to match supply with demand, but also to learn attributes of new arrivals in order to match them better in the future. We introduce a benchmark model with heterogeneous workers and jobs that arrive over time. Job types are known to the platform, but worker types are unknown and must be learned by observing match outcomes. Workers depart after performing a certain number of jobs. The payoff from a match depends on the pair of types and the goal is to maximize the steady-state rate of accumulation of payoff. Our main contribution is a complete characterization of the structure of the optimal policy in the limit that each worker performs many jobs. 4 - Pricing and Matching with Forward Looking Buyers and Sellers Yiwei Chen, Singapore University of Technology and Design, Singapore, Singapore, yiwei_chen@sutd.edu.sg, Ming Hu We study a dynamic market for a single product or service in which buyers with heterogeneous private valuations and sellers with heterogeneous private supply costs dynamically arrive. A single market-making intermediary decides dynamically on the ask and bid price that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. We use the mechanism design methodology to analyze the problem. We propose a simple matching and pricing policy that is asymptotically optimal. Under this policy, buyer and sellers behave myopically. 332C Health Care, Therapy and Treatment Contributed Session Chair: Ning Liu, Pennsylvania State University, University Park, PA, United States, nul147@psu.edu 1 - Barriers to use Clinic Decision Support and Computerized Provider Entry Among Physician Practices SD13 In this study, using a unique dataset from Minnesota, we longitudinally analyzed the major challenges faced by physician practices in adopting and using two main HIT functionalities - Clinical Decision Support (CDS) and Computerized Provider Order Entry (CPOE). We found that although the adoption rate increased between 2014 and 2016, the proportion of physician practices facing challenges in using these two functionalities also increased. We identified several major challenges reported in the data, and examined the physician practice characteristics associated with these challenges. 2 - Predicting Hospital Participation in Accountable Care Organizations Onyi Nwafor, University of Houston, 5710 Ballina Canyon Ln, Houston, TX, 77041, United States, onyid.nwafor@gmail.com, Norm Johnson Hospitals are increasingly joining Accountable Care Organizations (ACOs). Evidence suggests that this trend is primarily due to the financial rewards ACOs offer these hospitals when they are able to save costs without diminishing quality of care. Though such rewards may explain why hospitals join ACOs, the profile of hospitals that join tend to differ. Not knowing what this profile is for members and non-members of ACO implies that the net value of participation in ACOs is not clear. To address this problem, we conduct a study that uses a data-driven technique and a large data set to uncover the profile of these two groups. Our analysis reveals a new standard for entities seeking to join ACOs. Yunfeng Shi, Assistant Professor, The Pennsylvania State University, 504E Ford Building, University Park, PA, 16802, United States, yus16@psu.edu

332A Economics of Queueing Systems: Learning, Pricing, Priorities, and Reneging Sponsored: Manufacturing & Service Oper Mgmt, Service Operations Sponsored Session Chair: Philipp Afeche, University of Toronto, Toronto, ON, M5S 3E6, Canada, afeche@rotman.utoronto.ca Co-Chair: Shiliang Cui, Georgetown University, McDonough School of Business, Washington, DC, 20057, United States, shiliang.cui@gmail.com 1 - Learning and Earning for Congestion-prone Service Systems N. Bora Keskin, Duke University, Fuqua School of Business, 100 Fuqua Drive, Durham, NC, 27708-0120, United States, bora.keskin@duke.edu, Philipp Afeche Consider a firm selling a service in a congestion-prone system to price- and delay- sensitive customers. The firm faces Bayesian uncertainty about the consumer demand for its service and can dynamically make noisy observations on the demand. We characterize the structure and performance of the myopic Bayesian policy and well-performing variants. 2 - Coverage, Coarseness and Classification: Determinants of Social Efficiency in Priority Queues Martin Lariviere, Northwestern University, 2001 Sheridan Road, Evanston, IL, 60208, United States, m-lariviere@kellogg.northwestern.edu, Itai Gurvich, Can Ozkan In a service system with a continuum of customers, a decision maker must determine how much of the market to cover, how coarse a priority scheme to offer, and how to classify admitted customers to priority levels. We show that differences between revenue maximization and social optimality are largely questions of classification. 3 - Invite Your Friend and You’ll Move Up in Line The referral priority program, an emerging business practice adopted by a growing number of technology companies that manage a waitlist of customers, enables existing customers on the waitlist to gain priority access if they successfully refer new customers to the waitlist. We study the effectiveness of such a scheme as a marketing tool for customer acquisition and an operational approach for waitlist management. 4 - In-Queue Observation and Abandonment Shiliang Cui, Georgetown University, Washington, DC, 20057, Waiting in a line for service is not pleasant. Reneging happens when the initial estimated delay was tolerable so a customer joined the queue upon arrival. However, after spending some time waiting, the customer realizes that the actual service speed is slower than what he anticipated and the revised prediction of waiting time has exceeded his tolerance threshold. We investigate in this paper whether and when a revenue-maximizing server is in its best interest to reveal service speed information to customers upfront in the presence of such reneging behavior. Luyi Yang, Johns Hopkins University, Baltimore, MD, United States, luyi.yang@jhu.edu, Laurens G.Debo United States, Shiliang.Cui@georgetown.edu, Senthil Veeraraghavan, Jinting Wang, Yu Zhang, Yu Zhang 332B Incentives in Queueing and Sharing Platforms I Sponsored: Manufacturing & Service Oper Mgmt, Service Operations Sponsored Session Chair: Amy R. Ward, University of Southern California, Los Angeles, CA, 90089-0809, United States, amyward@marshall.usc.edu Co-Chair: Rouba Ibrahim, University College London, London, WC1E 6BT, United Kingdom, rouba.ibrahim@ucl.ac.uk 1 - Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework Daniel Freund, Cornell University, Ithaca, NY, 14850, United States, df365@cornell.edu, Siddhartha Banerjee, Thodoris Lykouris Optimizing shared vehicle systems is more challenging compared to traditional resource allocation due to complex network externalities. In particular, pricing/rebalancing in any location affects future supply throughout the system within short timescales. Such externalities are captured by steady-state Markovian models. However, using such models to design control policies is SD12

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