2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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TC75
Legends C- Omni
Behavioral Operations III
Contributed Session
Chair: Sam Kirshner, UNSW Business School, Level 2, West Wing,
Quadrangle Building, UNSW, Sydney, 2052, Australia,
1 - The Price Of Privacy: Experimental Evidence For The Value Of
Privacy With Respect To Social Norms
Rachel Cummings, California Institute of Technology, 1200 E
California Boulevard, MC 305-16, Pasadena, CA, 91125,
United States,
In a series of multi-player experiments, we measure people’s willingness to pay
for privacy, and how this value depends on behavioral social norms. Each player
is given the option to ``steal’’ monetary payments from the other players; the
information shared about these decisions varies across treatments, which includes
revealing partial or noisy information information about the player’s actions. By
varying the information sharing policy, we can measure how people trade off
money for privacy. We also measure how people’s willingness to steal changes as
stealing behavior becomes more prevalent.
2 - Behavioral Analysis Of Consumers’ Purchase Timing Decision
Ilhan Emre Ertan, PhD Candidate, University of Texas at Dallas,
430 Southwest Pkwy., Apt 2102, College Station, TX, 77840,
United States,
From a long history of markdown sales, consumers have expectations that
retailers will provide markdown discounts during a selling horizon. The consumer
purchase timing decision is analyzed by using discounted expected utility theory.
The consumer’s sequential decision-making process is formalized under uncertain
product availability and several behavioral biases. An optimal purchase timing
policy is identified in a market environment, in which a strategic customerknows
the markdown pricing scheme, available inventory level, and remaining time to
the end of the selling horizon.
3 - A Behavioral Experiment On Sharing Advance Warning Of A
Supply Chain Disruption
Sourish Sarkar, Assistant Professor, Pennsylvania State University -
Erie, 5500 Copper Dr, Apt 104, Erie, PA, 16509, United States,
, Sanjay Kumar
Using the beer distribution game in a controlled laboratory setting, we explore the
effect of advance warning of a supply chain disruption. Effect of sharing that
information with supply chain partners is also investigated. Considering both
upstream and downstream disruptions, we summarize the results from our
experiment with several scenarios: disruption with no advance warning but
information sharing; disruption with no advance warning and no information
sharing; disruption with advance warning but no information sharing; disruption
with advance warning and sharing of this warning information.
4 - The Behavioral Traps Of Making Multiple, Simultaneous,
Newsvendor Decisions
Shan Li, Assistant Professor, Baruch College, City University of
New York, Baruch College, 55 Lexington Avenue, New York, NY,
10010, United States, , Kay-Yut Chen
We conducted an experimental study to explore behaviors of newsvendors who
make order decisions for two stores simultaneously. While the two stores are
independent, we find that order decisions are impacted not only by the history
from the same store, but also by the past information from the other store.
5 - The Impact Of Reference Points On Supply Chain Coordination
Sam Kirshner, UNSW Business School, Level 2, West Wing,
Quadrangle Building, UNSW, Sydney, 2052, Australia,
, Lusheng Shao
Prospect theory and reference points have recently been utilized to explain the
behavioral ordering of human newsvendors. Adopting this approach to modeling
newsvendor behavior, we analytically explore the implications of reference points
in a two-tier supply chain. We show that reference points enable coordination in
a wholesale contract setting, and demonstrate how the reference points alter
coordinating contracts under buy-backs and revenue sharing agreements.
TC76
Legends D- Omni
Decision Analysis I
Contributed Session
Chair: Chih-Yang Tsai, Professor, State University of New York at New
Paltz, 1 Hawk Drive, New Paltz, NY, 12561-2443, United States,
1 - General Model For Dynamic Learning Ambiguity vs Bayesianism
Mohammad Rasouli, University of Michigan, 430 South Fourth
Ave, Ann Arbor, MI, 48104, United States,
We propose a general framework for adaptive learning that can model both
Bayesian and non-Bayesian learning. We show how different objectives including
expected outcome, minmax, and min regret can be modeled in this framework.
This framework gives a unified view to the existing results in learning. We
complete the existing results by proposing new sufficient statistics and dynamic
programming techniques. The connection with zero-sum games is discussed. We
will discuss conditions under which pure strategies can achieve optimal
performance.
2 - Remove A Paradox In Data Envelopment Analysis
Dariush Khezrimotlagh, Dr., Miami University, Oxford, OH, 45056,
United States,
Data Envelopment Analysis (DEA) is a non-parametric linear programming tool
to assess the relative efficiency of a set of homogenous firms with multiple input
factors and multiple output factors. DEA has been used in thousands of published
papers and books in well-known qualified journals and by reputable publishers
since 1978. DEA assumes that the relationships between the factors can be varied
from one firm to another. This article proves that this assumption has a
contradiction with the homogeneity of firms and concludes that the provided
scores by DEA models are not relatively meaningful and should not be used to
rank or benchmark firms. The instructions to remove the paradox in DEA are
illustrated.
3 - Reproducing Kernel Hilbert Space Approach To Stochastic
Frontier Semiparametric Estimation
Carlos Felipe Valencia Arboleda, University of los Andes, Cra 1
Este No 19A - 40, Edificio Mario Laserna, Bogota, 11001000,
Colombia,
We develop a nonparametric estimator for the Stochastic Frontier Analysis
problem based on the Reproducing Kernel Hilbert Space approach. We prove
minimax optimality of convergence for the frontier estimator, and semiparametric
efficiency for the finite dimensional parameters. Using Sobolev Hilbert Spaces, we
implement the method under monotonicity and concavity constrains. We
perform a simulation study to show the benefits of our estimators.
TC77
Legends E- Omni
Opt, Integer Programing III
Contributed Session
Chair: John Shane Lyons, PhD Student, Western University-Ivey
Business School, 23 Pine Ridge Drive, London, ON, N5X 3G7, Canada,
1 - Computer-assisted Discovery And Automated Proofs Of Cutting
Plane Theorems
Yuan Zhou, University of California - Davis, One Shields Avenue,
Davis, CA, 95616, United States, ,
Matthias Koeppe
Inspired by the breakthroughs of the polyhedral method for combinatorial
optimization in the 1980s, generations of researchers have studied the facet
structure of convex hulls to develop strong cutting planes. We ask how much of
this process can be automated: In particular, can we use algorithms to discover
and prove theorems about cutting planes? We focus on general integer and mixed
integer programming, and use the framework of cut-generating functions. Using a
metaprogramming technique followed by practical computations with
semialgebraic cell complexes, we provide computer-based proofs for old and new
cutting-plane theorems in Gomory-Johnson’s model of cut generating functions.
TC77