2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Assortment Optimization For Choosy Customers
Jake Feldman, Washington University in St. Louis,
We study two different choice models that capture the purchasing behavior of
customers who only consider purchasing one of two substitutable products. We
refer to these customers as choosy. The first choice model captures substitution
behavior through probabilistic transitions between products. The second choice
model that we study assumes each customer is characterized by a ranking of the
products. An arriving customer will purchase her highest ranked product that is
offered. Since we model choosy customers, we assume that these rankings
contain at most two products. This paper focuses on the assortment optimization
problem under these two choice models.
3 - Learning Consumer Tastes From Dynamic Assortments:
A Nonparametric Bayesian Model
Canan Ulu, Georgetown University,
Dorothee Honhon
We study dynamic assortment decisions of a firm learning about consumer tastes
by observing sales. Each period, the firm offers an assortment to maximize
expected total profits over a finite horizon given its beliefs on consumer tastes.
The consumers then choose a product that maximizes their utility and the firm
updates its beliefs on consumer tastes after having observed sales. We model
consumer tastes as locations on a Hotelling line and develop a nonparametric
Bayesian learning model using Polya tree priors. We develop upper bounds on the
firm’s total profit based on information relaxations and study the performance of
various heuristic policies with respect to these upper bounds.
4 - The Price Of Flexibility
Hoda Bidkhori, Swanson School of Engineering, University of
Pittsburgh, Pittsburgh, PA, United States,
Dimitris Bertsimas, Albert Dunning
Process flexibility is a popular operations strategy that has been employed in
many industries to help firms respond to uncertainty in product demand.
Additional flexibility comes at a cost that firms must balance against the reduction
of risk it can provide. We reduce the price of flexibility by taking an optimization
approach to the process flexibility design problem. Unlike many approaches in the
literature, we consider systems that may have nonhomogenous parameters and
unbalanced capacity and demand. We formulate the problem as a robust adaptive
optimization model, and propose a computationally tractable method for solving
this model using standard integer optimization software.
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206A-MCC
Collaborative New Product Development
Sponsored: New Product Development
Sponsored Session
Chair: Niyazi Taneri, Singapore University of Technology and Design,
Singapore, Singapore, na, Singapore,
1 - The Role Of Operations In Alliances For New
Product Development
Niyazi Taneri, Singapore University of Technology and Design,
, Arnoud De Meyer
We review contract theory and hypothesize its implications for the choice
between collaborative alliances (where both parties exert joint efforts) and
sequential alliances (where, for the most part, the partner takes over going
forward). We test these hypotheses through the analysis of a dataset of over 2000
biopharmaceutical alliances.
2 - Optimal Sequential Investments In Product Development With
Exogenous Technologies And Learning
Shantanu Bhattacharya, Singapore Management University,
, Stylianos Kavadias, Sameer Hasija
We determine the optimal investments for a firm when the product development
opportunities come over time from two distinct exogenous technologies. Upfront
investment in a product platform from a technology that is currently available
gives higher returns from opportunities based on the platform technology in the
future, due to the associated learning effects. We formulate the resource
allocation problem and characterize the optimal development investments that
determine the firm product development roadmap. We show that the firm’s
optimal resource investment in platform development has a nuanced relationship
with the relative speed of arrival of the new technology.
3 - Business Model For Technology-intensive Supply Chains
Junghee Lee, University of California, San Diego,
Krishnan Vish, Hyoduk Shin
In technology licensing, controversy has swirled among firms and policymakers
about royalty base choice between subsystem and full system. We study the
impact of royalty base on innovator’s business model decisions from R&D
investment to manufacturing integration in Technology Intensive Supply Chain.
We identify the key drivers, market heterogeneity and production cost, for the
controversy and provide managerial and political implications. Interestingly, the
innovator can be better off with a strong competitor when market inequality is
low or the competitor is strong enough.
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207A-MCC
Market Microstructure and Optimal Trading
Sponsored: Applied Probability
Sponsored Session
Chair: Costis Maglaras, Columbia University, New York, NY,
United States,
Co-Chair: Ciamac Cyrus Moallemi, Columbia University, New York, NY,
United States,
1 - Trading The Close — Market Impact And Optimal Execution
Costis Maglaras, Columbia University,
The “close” concentrates a significant amount of daily liquidity for various market
structure reasons. In this talk I will describe a market impact model for “Market-
On-Close” (MOC) orders, and its consequences on optimal execution profiles.
2 - Portfolio Liquidity Estimation And Optimal Execution
Kai Yuan, Columbia University,
We develop a tractable model to estimate portfolio liquidity costs through a multi-
dimensional generalization of the optimal execution model of Almgren and
Chriss. Our model allows for the trading of standardized liquid bundles of assets
(e.g., ETFs or indices). We show that in a “large universe” asymptotic limit, where
the correlations across a large number of assets arise from relatively few
underlying common factors, the liquidity cost of a portfolio is essentially driven
by its idiosyncratic risk. Moreover, the additional benefit of trading standardized
bundles is roughly equivalent to increasing the liquidity of individual assets.
3 - Optimal Execution In Hong Kong Given A Market-on-close
Benchmark
Christoph Frei, University of Alberta, Edmonton, AB, Canada,
, Nicholas Westray
For stocks traded on the Hong Kong Exchange, the median of five prices taken
over the last minute of trading is currently chosen as the closing price. We
introduce a stochastic control formulation to target such a median benchmark in
an empirically justified model which takes the key microstructural features into
account. We solve this problem by providing an explicit and efficient algorithm
which can be used for the dynamic linear approximation of any square-integrable
random variable. Implementing the algorithm on the stocks of the Hang Seng
Index, we find an average improvement of around 6% in standard deviation of
slippage compared to an average trader’s execution.
4 - Mean Field Games Of Singular Control With Applications
Joon Seok Lee, UC Berkeley, 2938 McClure Street, # A207B,
Oakland, CA, 94609, United States,
Xin Guo
We introduce a mean field game framework with singular controls. To solve this
singular control problem with multiple agents, we derive the Kolmogorov
forward equation for the singular control, which is a generalization of the mean fi
eld game with regular controls. Both single controls with a bounded velocity and
singular controls with a finite variation will be analyzed. Finally, we will present
some applications to real options and systemic risk.
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207B-MCC
Computational Issues in Productivity and
Efficiency Measurement
Invited: Data Envelopment Analysis
Invited Session
Chair: Jose Dula, Virginia Commonwealth University, Snead Hall, 301
W. Main Street, Richmond, VA, 23284, United States,
1 - Validating DEA As A Rating Tool: The Case Of CMS’s Nursing
Home Compare.
Jose Dula, Virginia Commonwealth University,
Marie-Laure Bougnol
The US government’s CMS agency rates more than 15000 nursing homes
nationwide using a star system. The outcomes are disseminated in various ways
including a user friendly and informative web page. We report on a study
comparing the government’s ratings with classifications obtained with DEA using
the same model and data. We answer the question: How would DEA fare as a tool
to rate complex entities such as nursing homes?
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