2016 INFORMS Annual Meeting Program

MD35

INFORMS Nashville – 2016

MD35 205A-MCC Empirical Service Operations Sponsored: Manufacturing & Service Oper Mgmt,

2 - Learning And Impatience In Queues Senthil Veeraraghavan, Wharton School of the University of Pennsylvania, senthilv@upenn.edu, Li Xiao, Hanqin Zhang We study the abandonment behavior of customers in M/M/1+G by the Bayesian learning approach. An arriving impatient customer knows the average arrival rate but does not know the average service rate. To have a rational abandonment, the arriving impatient customer has to learn the service rate. Two Bayesian learning ways are discussed in accordance with what kind information of the queueing system is available to the arriving impatient customer. Based on the learned service rate, the abandonment behavior is quantitatively characterized by the utility of the arriving impatient customer. 3 - Jumping The Line, Charitably: Analysis And Remedy Of The Donor Priority Rule Tinglong Dai, Assistant Professor, Johns Hopkins University, 100 International Drive, Baltimore, MD, 21202, United States, dai@jhu.edu, Ronghuo Zheng, Katia P. Sycara In the United States, the growth of the organ transplantation waiting list significantly outpaces the supply of donated cadaveric organs. Among a myriad of initiatives aiming to boost the supply, the donor priority rule, under which a registered organ donor, in case of needing a transplant, is given priority to receive a donated organ, has been weighed by U.S. policy makers. In this paper, we model the U.S. organ donation and allocation system using the strategic queueing theoretic framework. We propose a simple freeze-period mechanism, and prove that in conjunction with the donor priority rule, it can increase the supply of donated organs without compromising the average quality of the donor pool. 4 - Learning And Earning For Congestion-prone Service Systems N. Bora Keskin, Duke University, Durham, NC, 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. MD37 205C-MCC Sustainability in Supply Chains Sponsored: Manufacturing & Service Oper Mgmt, Sustainable Operations Sponsored Session Chair: Georgia Perakis, Massachusetts Institute of Technology, Cambridge, MA, United States, georgiap@mit.edu Co-Chair: Maxime Cohen, Google NYC, New York, NY, United States, maxccohen@gmail.com 1 - Optimal Stopping Of Subsidies To Products With Network Externality Ningyuan Chen, HKUST, Hong Kong, Hong Kong, nychen@ust.hk, Saed Alizamir, Vahideh Manshadi Many products exhibit network externality: a customer who has purchased the product makes his/her neighbors or friends more likely to buy the same product. This includes eco-friendly products such as electronic cars and solar panels. The government subsidizes customers to promote such products. We find that it is optimal for the government to stop the subsidy when the total externality of the owners reaches a threshold, which depends on the spectrum of the externality matrix. The optimal stopping time is not monotone in the strength of the externality between customers. We investigate how the structure of the network affects the stopping time and the optimal reward of the government. 2 - A Unifying Framework For Consumer Surplus Under Demand Uncertainty Charles Thraves, MIT, cthraves@mit.edu, Maxime Cohen, Georgia Perakis We present a general framework for consumer surplus when demand is stochastic and there are multiple items. We take a utility maximization approach in order to study the impact of demand uncertainty on consumers in several interesting settings. We show how the impact of uncertainty on consumers depends on the demand shape (convexity) and the allocation rule. In many settings we show that it can in fact hurt consumers.

Service Operations Sponsored Session

Chair: Robert Louis Bray, Kellogg School of Management, 830 Hinman Ave, Apt 2S, Evanston, IL, 60202, United States, robertlbray@gmail.com 1 - Modeling Growth In Service Operations: Evidence From The App Economy Ken Moon, The Wharton School, Philadelphia, PA, United States, kenmoon@wharton.upenn.edu, Haim Mendelson The best service firms expand and sustain their customer bases and profits organically through word of mouth and customer retention. We propose a customer-flow model fashioned after classical service operations models that focuses on the effects of customer retention, usage frequency, and growth (RFG). Using daily, weekly, and monthly usership data for services in the app economy, our results empirically demonstrate the importance of RFG, including that new apps score increasingly higher in growth by improving in service quality. Finally, we present evidence of an experience curve, analogous to that in manufacturing, whereby service events drive advances in service quality and RFG. 2 - Managing Product Quality In The Face Of Field Failures

Ahmet Colak, Northwestern University, Evanston, IL, United States, a-colak@kellogg.northwestern.edu

We model a manufacturer’s and regulator’s joint recall decisions as a dynamic discrete choice game. We estimate our model with 14,124 U.S. auto recalls and 976,062 defect reports over the period 1994—2015. We find that (i) automakers initiate recalls mainly to avoid field failure costs, and (ii) automakers don’t

preempt the regulator’s interventions in 86% of our sample. 3 - Impact Of Callers’ History On Abandonment: Model And Implications

Seyed Emadi, UNC - Kenan Flagler Business School, Chapel Hill, NC, 27599, United States, Seyed_Emadi@kenan-flagler.unc.edu, Jayashankar M Swaminathan Caller abandonment could depend on their past waiting experiences. To tease out the impact of callers’ waiting experiences on their abandonment behavior from the impact of their heterogeneity, we use a structural estimation approach in a Bayesian learning setting. Our framework has managerial implications at both tactical and operational levels such as managing customer expectation about their delays in the system, and implementation of patience-based priority policies such as Least-Patience-First and Most-Patience-First scheduling. 4 - Multitasking, Multi-armed Bandits, And The Italian Judiciary Robert Louis Bray, Kellogg School of Management, robertlbray@gmail.com We model how a judge schedules cases as a multi-armed bandit problem. The model indicates that a first-in-first-out (FIFO) scheduling policy is optimal when the case completion hazard rate function is monotonic. But there are two ways to implement FIFO in this context: at the hearing level or at the case level. Our model indicates the latter policy, prioritizing the oldest case, is optimal when the case completion hazard rate function increases. This result convinced six judges of the Roman Labor Court of Appeals—-a court that exhibits increasing hazard rates—-to switch from hearing-level FIFO to case-level FIFO. We estimate that our intervention decreased the average case duration by 12%. MD36 205B-MCC Control, Learning, and Strategic Behavior in Queueing Models Sponsored: Manufacturing & Service Oper Mgmt, Service Operations Sponsored Session Chair: Philipp Afeche, University of Toronto, 105 St. George Street, Toronto, ON, M5S 3E6, Canada, afeche@rotman.utoronto.ca 1 - Dynamic Control Of A Call Center With The Callback Option Xiaoshan Peng, University of Chicago Booth School of Business, x-peng@chicagobooth.edu, Baris Ata We investigate a call center with the callback option. An incoming customer is routed to an online queue or to an offline queue where she needs to hang up the phone and waits for the system to call her back. We characterize the optimal routing policy and service policy when the forecast of the arrival rate is available.

220