Informs 2015

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INFORMS Philadelphia – 2015
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3 - Institutional Logics Change and Firm Attention: Sustainability
Logic in the Apparel Industry
Yoojung Ahn, University of Massachusetts Amherst, MA,
121 Presidents Drive, Amherst, MA, 01003,
United States of America,
This paper explores how change in institutional logics impacts firm attention to
this logic to create self-regulatory institutions. I examine the “sustainability logic”
in the apparel industry to understand the different ways firms attend to this logic,
and whether attention patterns contribute differently to participating in a self-
regulatory institution. I deploy content analysis and event history analysis
methods.
4 - Development of Predictive Model for Moviegoers using Multi
Regression Analysis and Movie Scheduling
Sung Wook Yun, Yonsei University, Sinchon-dong,
Seodaemun-gu, Seoul, Korea, Republic of,
This paper is about a practical decision-making approach to a film screening in a
multiplex movie theater. Our ultimate objective in this paper is to maximize the
number of moviegoers by allocating movies to a limited number of screens that
have different number of seats. We specifically devised a movie schedule model
that determine which movies will be played on which screens with the
consideration of an exchange screening and a double booking based on the Movie
forecasting.
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43-Room 103A, CC
Pricing and Inventory Control
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: Izak Duenyas, John Psarouthakis Professor, University of
Michigan, Ross School of Business, Ann Arbor, MI, 48109,
United States of America,
1 - Dual-metric Segmentation for Creating Airline Forecast Groups
Wei Wang, Scientist, PROS, Inc., 3100 Main Street, Suite #900,
Houston, Tx, 77002, United States of America,
For airlines, forecast groups created based on various flight attributes can improve
forecast accuracy and provide sponsorship (especially to new markets), however
frequently the segmentation uses load factor (LF) as the only metric. We present a
two-metric and two-step approach where the segmentation is guided by both
revenue and LF.
2 - Analysis of Self-adjusting Controls for Dynamic Pricing with
Unknown Demand Parameters
George Chen, Stephen M. Ross School of Business, University of
Michigan, 701 Tappan Ave, Ann Arbor, MI, United States of
America, , Izak Duenyas, Stefanus Jasin
We study the network-RM pricing problem with unknown demand function
parameter. We develop a joint learning and dynamic pricing heuristic that
combines MLE and self-adjusting price control and show that the best attainable
revenue loss rate in the general setting can be achieved without re-optimization.
A much sharper rate can also be achieved when demand are well-separated using
the proposed self-adjusting heuristic.
3 - Data-driven Algorithms for Nonparametric Multi-product
Inventory Systems
Weidong Chen, University of Michigan, Industrial and Operations
Engineering, Ann Arbor, MI, 48109, United States of America,
, Cong Shi, Izak Duenyas
We propose a data-driven algorithm for the management of stochastic multi-
product inventory systems with limited storage as well as production cost
uncertainty. The demand distribution is not known a priori and the manager only
has access to past sales data. We measure performance of our proposed policy
through regret and characterize the rate of convergence guarantee of our
algorithm.
4 - Dynamic Pricing and Inventory Management under
Network Externalities
Renyu Zhang, Doctoral Student, Olin Business School,
Washington University in St. Louis, Campus Box 1133,
1 Brookings Drive, St. Louis, MO, 63130,
United States of America, , Nan Yang
We study a periodic-review joint pricing and inventory management model with
network externalities. The product is a network product so that the customers’
willingness-to-pay and, thus, the potential demand of the product are increasing
in the network size. We characterize the optimal policy and analyze the impact of
network externalities upon the optimal price and inventory decisions. We also
propose effective strategies to exploit network externalities.
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44-Room 103B, CC
New Approaches in Dynamic Pricing and Revenue
Management
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: Yonatan Gur, Stanford GSB, 655 Knight Way, Stanford, CA,
94305, United States of America,
1 - Randomized Markdowns and Online Monitoring
Ken Moon, PhD Candidate, Stanford GSB, 655 Knight Way,
Stanford, CA, 94305, United States of America,
Kostas Bimpikis, Haim Mendelson
Using data tracking customers of a North American retailer, we present empirical
evidence that consumers are forward-looking and that monitoring products
online associates with successfully obtaining discounts. Developing a structural
model relating consumers’ dynamic behavior to their monitoring costs, we find
substantial heterogeneity, with opportunity costs for an online visit ranging from
$2 to $25 in inverse relation to price elasticities. We show implications for retail
operations.
2 - Agent Behavior in the Sharing Economy: Evidence from Airbnb
Antonio Moreno-Garcia, Northwestern University,
2001 Sheridan Rd, Evanston, Il, 60208, United States of America,
Jun Li, Dennis Zhang
Using data from Airbnb, we study the behavior of non-professional agents in two-
sided platforms.
3 - Implications of Choice Paralysis on Operational Decision Making
Rene Caldentey, NYU, 44 W 4th St, New York, NY, 10012,
United States of America,
Srikanth Jagabathula, Anisha Patel
We empirically investigate the notion of choice paralysis (i.e., too many options
can paralyze a consumer and make them more prone to not purchasing) and
study its implications on assortment and inventory decisions. We propose a
modification to the nested logit model to incorporate the choice paralysis effect.
4 - Doing While Learning and Adapting to a Changing Environment
Yonatan Gur, Stanford GSB, 655 Knight Way, Stanford, CA,
94305, United States of America,
Omar Besbes, Assaf Zeevi
Multi Armed Bandit (MAB) problems are building blocks of many RM&P
problems. We study a MAB formulation that allows for a broad range of temporal
uncertainties in the rewards. We characterize the complexity of this class of
problems, mapping the ``budget” of allowable variation to the minimal achievable
regret relative to a dynamic oracle. We study the price of universality: the
additional complexity associated with not knowing variation budget, over the one
embedded in a known budget.
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45-Room 103C, CC
Reducing the Carbon Footprint
Contributed Session
Chair: Emre Berk, Bilkent University, Management Faculty, 06800
Bilkent, Ankara, Turkey,
1 - Real Options Portfolio Strategies for Cloud
Infrastructure Expansion
Yunpeng Pan, Assistant Professor, South Dakota State University,
Mathematics&Statistics, Box 2220, Brookings, SD, 57007,
United States of America,
Cloud services are powered by capital-intensive, energy-hungry data centers. The
temporal and spatial choices of data center deployment must be made judiciously
to best satisfy customer needs while keeping economic and environmental costs
in check. To this end, we propose a real options framework for evaluating the
desirability of candidate sites under the complex dynamics of electricity rate,
customer demand, etc.; we develop strategies for portfolio selection and option
exercise.
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