2016 INFORMS Annual Meeting Program

TB31

INFORMS Nashville – 2016

TB32 203A-MCC Revenue Mgt, Pricing III Contributed Session Chair: Fouad H Mirzaei, Santa Clara University, Unit 3, 559 Alviso St, Santa Clara, CA, 95050, United States, fhm.phd@ivey.ca 1 - Pricing In Remanufacturing Operations Akshay Mutha, Pennsylvania State University, Smeal College of Business, 454 Business Building, University Park, PA, 16802, United States, axm536@psu.edu, Saurabh Bansal, V Daniel R Guide We consider a firm that can remanufacture products after the demand is realized. We analyze the effect of postponing remanufacturing operations on the pricing decisions of a firm. We show the application of our model using industry data. 2 - Airline Price-sensitive Demand Forecasting And Optimization Sylvia Zhu, Sabre Airline Solution, 3150 Sabre Dr., Southlake, TX, 76092, United States, sylvia.zhu@sabre.com Traditional revenue management models assume the demand is independent. In recent years, there has been more attempt to handle scenarios in which the customer looks for the lowest available fare. It means that the demand for specific product depends on availability of other products. In order for us to make a recommendation on the capacity and connectivity that an airline will need to achieve higher revenue, we will need to provide reliable dependent demand forecasting and optimization methodologies. We will describe major features of the forecasting and optimization models for dependent demand revenue management and share experiments of their performance. 3 - An Analysis Of B2B Negotiations In The Context Of Data Products Jyotishka Ray, Student, University of Texas-Dallas, Naveen Jindal School of Management, Richardson, TX, 75083, United States, jxr114030@utdallas.edu, Syam Menon, Vijay S Mookerjee The explosive growth of eBusiness has allowed many companies to accumulate a repertoire of rich and unique data sets that can provide substantial value to other firms. We analyze how to monetize proprietary data products through negotiation. We consider whether the seller should make presentations to the buyer before the negotiation when the buyer is underestimating the value. We extend our study to understand the impact of a consultant (hired by the buyer to analyze the data) on the negotiation process. We adapt the generalization of the Nash bargaining problem to analyze this three-player negotiation. We find that the presence of a consultant reduces the possibility of a viable presentation. 4 - Managing Change Revenue With Presence Of Time Uncertain Customers Fouad H Mirzaei, Santa Clara University, Unit 3, 559 Alviso St, Santa Clara, CA, 95050, United States, fhm.phd@ivey.ca, Fredrik Odegaard, Xinghao Yan In this study, we focus on the dynamics between a firm charging a change fee and customers who are uncertain about their future travel plans. While the firm maximizes its revenue by imposing optimal change fees, customers consider their travel plan uncertainties and maximize their utilities by responding strategically to these fares. Without imposing any distributional assumptions, we analytically derive each market player’s best reaction to the other to prescribe the characteristics of the firm/customer interaction equilibrium. We also investigate how the optimal monopolistic price should be set with the presence of a change fee. TB33 203B-MCC Queueing Models I Contributed Session Chair: Yao Yu, North Carolina State University, 4335-3 Avent Ferry Road, Raleigh, NC, 27606, United States, yyu15@ncsu.edu 1 - Can The Way Customers Are Assigned To Servers Affect The Unscheduled Within-day Work Breaks In A Service System? Xu Sun, Columbia University, 363 W 123rd St, Apt 4R, New York, NY, 10027, United States, xs2235@columbia.edu, Ward Whitt We apply many-server heavy-traffic analysis to study the impact of alternative routing rules, such as longest-idle-server-first (LISF) and randomized routing (RR), on the pattern of server idleness in a service system. We show that LISF provides more regular breaks than RR when the staffing is adequate to allow non- negligible idleness.

2 - Disease Trend Prediction And Resource Allocation for Optimal Containment Eva Lee, Georgia Tech, evakylee@isye.gatech.edu, Kevin Liu This work is joint with CDC. This work focuses on a computational decision modeling framework that integrates a biological disease spread predictive model, a dynamic network-based social-behavior model that captures human behavior and interaction, and a stochastic queueing model that describes treatment characteristics, day-to-day hospital and homecare processes, and resource usage (labor, time, and equipment). The computational platform includes an optimization engine that determines the minimum resource requirements needed to contain the epidemic. Results of its usage for Ebola control in West Africa will be presented. 3 - An Examination Of Early Transfers To The ICU Based On A Physiologic Risk Score Wenqi Hu, Columbia Business School, New York, NY, United States, wh2274@columbia.edu, Carri Chan, Jose Zubizarreta, Gabriel J. Escobar Unplanned transfers of patients from the ward to the Intensive Care Unit (ICU) can occur due to rapid deterioration and may increase the patients’ risk of death and lengths of stay in hospital. A new predictive model, the EDIP2, was developed with the intent to identify patients at risk for deterioration, which in some cases could trigger a proactive transfer to the ICU. This work examines the potential costs and benefits of preventive ICU admissions based on this new dynamic warning system. We find that preventive ICU admissions have the potential to improve patient outcomes, and physicians’ fears of needlessly clogging the ICU may not be as dire as initially feared. 4 - Approaches To Manage Demand Variability In An Academic Medical Center Ryan M. Graue, Sr. Project Manager, Process Improvement, Beth Israel Deaconess Medical Center, Boston, MA, United States, rgraue@bidmc.harvard.edu, Sarah Moravick, Julius Yang Hospitals struggle to manage variability in patient demand. For one, their physical supply (e.g., bed capacity and number of exam rooms) to accommodate patients is essentially fixed, while the daily inpatient census and number of appointments in each clinic fluctuate. In addition, minimal flexibility is built into staffing models, leading to wide day-to-day swings in staff productivity. Our work focuses firstly on approaches to reduce demand variability, and secondly on developing a newsvendor staffing model formulation that evaluates the required staff-to- patient ratios in different clinical areas and aims to minimize the costs of overstaffing and understaffing.

TB31 202C-MCC

Topics in Operations and Finance Interface Sponsored: Manufacturing & Service Oper Mgmt, iFORM Sponsored Session Chair: S. Alex Yang, London Business School, Regent’s Park, Sussex Pl,

London, NW1 4SA, United Kingdom, sayang@london.edu 1 - The Dual Objectives Of Reward-based Crowdfunding Rachel Rong Chen, University of California-Davis, rachen@ucdavis.edu, Esther Gal-Or, Paolo Roma

Reward-based crowdfunding can provide entrepreneurs information regarding future demand for their products. Such information is valuable, especially for entrepreneurs who need additional funding from a Venture Capitalist (VC), because a successful crowdfunding campaign can help convince the VC to finance the project. This paper examines the optimal campaign design when the crowdfunding campaign serves the dual objectives of raising capital and acquiring market information. 2 - Optimal Pricing And Efficiency In Group Buying: Theory And Evidence Liu Ming, PhD Candidate, Unversity of Maryland, College Park, MD, 20742, United States, liu.ming@rhsmith.umd.edu, Tunay Tunca We study demand risk mitigation by utilization of group buying in retail sales. We model a frequently employed group buying mechanism and derive the dynamic consumer behavior and optimal seller pricing. Utilizing data from a major retail platform, we structurally estimate the model and calculate the efficiency gains employed by the mechanism. 3 - Pass-through Contracts Volatile Inputs And Frictions Danko Turcic, Washington Univ. in St. Louis, turcic@wustl.edu, Panos Kouvelis, Wenhui Zhao This paper examines puts forth a risk-management framework for a supply chain exposed to both demand and input cost risks. In the setting that we consider, the supply chain participants interact via index contracts that allow for re-distribution of price risks. We identify conditions under which firms find it optimal to re- distribute risk and conditions under which firms want to both re-distribute and hedge.

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