2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Disease Trend Prediction And Resource Allocation for
Optimal Containment
Eva Lee, Georgia Tech, , 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, 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,
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,
1 - The Dual Objectives Of Reward-based Crowdfunding
Rachel Rong Chen, University of California-Davis,
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,
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, ,
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.
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,
1 - Pricing In Remanufacturing Operations
Akshay Mutha, Pennsylvania State University, Smeal College of
Business, 454 Business Building, University Park, PA, 16802,
United States, , 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,
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,
, 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,
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,
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, , 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.
TB31