Informs Annual Meeting 2017

SA21

INFORMS Houston – 2017

SA22

3 - Directionality in Online Social Networks Nitin Mayande, Nike, 6249 NE Carillion Drive, Unit 201, Hillsboro, OR, 97124-8097, United States, nitin.mayande@nike.com, Charles M. Weber Relationships within social networks have been treated as reciprocal relationships in most cases within extant theory (Burt, 1976, Burt and Doreian, 1982, Granovetter, 1973). There have been a few studies in which directionality has been taken as a factor (Allen, 1977, Roger, 1983). However, the impact of considering directionality in a social network has not been studied in detail. In order to understand the impact of considering directionality, this study bifurcates the process of interaction (reciprocal) into consumption phase and propagation phase. The early results show that within a directed network, the consumption and propagations phases can behave very differently from each other. 4 - Measuring the Modularization of Industrial Service Processes Juliana Hsuan, Professor, Copenhagen Business School, Solbjerg Plads 3 B.5.27, Frederiksberg, DK-2000, Denmark, jh.om@cbs.dk, Thomas Frandsen We examine how complex industrial service processes can be measured through the lenses of service modularization strategies. The measured degree of modularity sharpened our understanding of the role of standardization and component reuse on creating architecture flexibility. It also provided a foundation for analyzing the short- and long-term impact of service innovation. 342C Appointment Scheduling with No-Show and Unpunctual Patients Invited: InvitedHealthcare Systems and Informatics Invited Session Chair: Paul Brooks, Virginia Commonwealth University, Richmond, VA, 23284, United States, jpbrooks@vcu.edu 1 - Multimodularity in the Stochastic Appointment Scheduling Problem with Discrete Arrival EPOCHS Christos Zacharias, Assistant Professor, University of Miami, 5250 University Drive, Room 401 K/E, Coral Gables, FL, 33146, United States, czacharias@bus.miami.edu, Tallys Yunes We address the problem of designing appointment scheduling strategies that account for patients’ no-show behavior, non-punctuality, emergency walk-ins and general stochastic service times. We demonstrate that the optimal scheduling strategy minimizes a multimodular function and how to perform local search in polynomial time. 2 - Overbooking Rules in Outpatient Clinics when Considering No-shows and Cancellations Shannon Harris, The Ohio State University, 1005 W. 5th Avenue, In this paper we propose rules for overbooking patients in an outpatient clinic over a scheduling horizon. We incorporate direct and indirect waiting time, overtime, no-shows, and cancellations to inform the overbooking decisions. Rules for overbooking up to two patients per day, when making a pair-wise decision between two days, are expressed analytically. Numerical solutions for overbooking greater than two patients are provided. The effect of no-shows and cancellations on overbooking is also discussed. 3 - Predictive Analysis of Missed Medical Appointments/no-shows Asil Oztekin, University of Massachusetts Lowell, 333 1st Street, Unit 210, Lowell, MA, 01850-2580, United States, asil_oztekin@uml.edu Patients not showing up for clinical appointments (No-Shows) is a huge drain on the healthcare resources and costs several millions nationwide. The dataset has 300,000 medical appointment records and 15 variables. The purpose of this analysis is to predict whether a patient would show up for his appointment or not and to investigate why patients miss their scheduled appointments. The CRISP- DM approach is adopted to provide a framework to ensure potentially-useful deliverables for medical practice. This study aims to recognize if there are any key patterns to this type of “no-show” behavior. SA21 Unit 535, Columbus, OH, 43212-3085, United States, harris.2572@osu.edu, Jerrold H. May, Luis G. Vargas

342D Economic Models in Revenue Management Sponsored: Revenue Management & Pricing Sponsored Session Chair: Dan Zhang, University of Colorado, Boulder, CO, 80309, United States, dan.zhang@colorado.edu Co-Chair: Xiao Huang, John Molson School of Business, Concordia University, Montreal, QC, H3G 1M8, Canada, xiao.huang.2009@marshall.usc.edu 1 - An Analytical and Empirical Look at Hotel Standby Upgrades Pelin Pekgun, University of South Carolina, Columbia, SC, 29208, United States, Pelin.Pekgun@moore.sc.edu, Ovunc Yilmaz, Mark Ferguson, Guangzhi Shang In this research, we first develop an analytical model of premium room and standby upgrade pricing under an uncertain market size, and examine how and when standby upgrades can provide additional revenue for a hotel. We then follow up with an empirical study, utilizing a hotel chain’s booking and standby upgrades data, to understand the decision-making process of a guest facing standby upgrades, and the extent of the strategic guest behavior. Our findings provide guidance for hoteliers to use standby upgrade programs more effectively based on the hotel type and guest behavior. 2 - Created Unequal Bundling with Crowdsourced Products Lu Wang, University of Toronto, Rotman School of Management, 105 St. George Street, Toronto, ON, M5S.3E6, Canada, lu.wang12@rotman.utoronto.ca, Ming Hu As consumer buying habits are trending toward more simple and hassle-free experiences, more and more companies are jumping into the innovative business model of subscription services. Subscription providers such as Spotify, Netflix and OneGo (an all-you-can-fly subscription service provider) crowdsource products/services from many vendors and bundle them for the price of one. The collected subscription fees for the bundle then are allocated according to the realized contributions by each crowdsourced product. We examine the incentive compatibility of different parties under various bundling strategies. 3 - Service Product Design and Consumer Refund Policies Xiao Huang, John Molson School of Business, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC, H3G 1M8, Canada, xiao.huang@concordia.ca, Dan Zhang We consider a firm serving heterogeneous consumers with imperfect signals on their quality valuations. The firm can customize offering qualities, customize refund rates, or customize both. We show that a wide range of menu can be optimal depending on valuation heterogeneity and signal quality. Even with the liberty of customization, the firm may still offer standard quality or refund policy. Interestingly, aggregate consumer surplus does not increase with generous refund, and the seller may not have incentive to improve the signal quality even if it is cost-less. 4 - Managing Hotel Cancellations Dan Zhang, University of Colorado, Leeds School of Business, University of Colorado, Boulder, CO, 80309, United States, dan.zhang@colorado.edu, Xiao Huang, Jian Wang We use hotel transaction data to investigate customer cancellation behavior. We show that cancellation rates are highly correlated with booking rates and the posted rates at the time of cancellation. We discuss the implications of our findings on hotel pricing policies.

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