Informs 2015

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INFORMS Philadelphia – 2015
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2 - The Value of Fit Information in Online Retail
Antonio Moreno-Garcia, Northwestern University, 2001 Sheridan
Rd, Evanston, Il, 60208, United States of America,
Santiago Gallino
We conduct a field experiment to quantify the value of fit information in online
retail.
3 - The Effect of Music Labels on Song Popularity in Electronic
Markets Without Barriers-to-Entry
Marios Kokkodis, Assistant Professor, Boston College,
34 E 10th, New York, NY, 10009, United States of America,
In this work we study the effect of music labels on song popularity in electronic
markets without barriers to entry.
4 - The Moderating Effects of Product Attributes and Reviews on
Recommender System Performance
Dokyun Lee, Carnegie Mellon University, United States of
America, Kartik Hosanagar
We investigate the moderating effect of several product attributes on the efficacy
of a recommender system for increasing conversion rate via a field experiment on
one of the top North American retailer’s website. Our results provide many
managerial implications on when to utilize recommenders and how
recommenders interact with reviews and product attributes.
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26-Room 403, Marriott
INFORMS Undergraduate Operations Research
Prize II
Cluster: INFORMS Undergraduate Operations Research Prize
Invited Session
Chair: Aurelie Thiele, Lehigh University, 200 W Packer Ave,
Bethlehem, PA, 18015, United States of America,
1 - Piecewise Static Policies for Two-stage Adjustable Robust
Optimization Problems under Uncertainty
Omar El Housni, Industrial Engineering and Operations Research,
Columbia University, 547 Riverside Drive Apt. 1B,
New York, NY, 10027, United States of America,
, Vineet Goyal
We consider two-stage adjustable robust linear optimization problems under
uncertain constraints and study the performance of piecewise static policies. We
show that surprisingly there is no piecewise static policy with polynomial number
of pieces with performance significantly better than a static policy in general. We
also present a family of piecewise static policy with exponential pieces that has a
significantly better performance than a static solution and admits a compact MIP
formulation.
2 - Multi-step Bayesian Optimization for One-dimensional
Feasibility Determination
Massey Cashore, University of Waterloo, 200 University Ave
W, Waterloo, On, N2L 3G1, Canada, ,
Peter Frazier
Bayesian optimization methods allocate limited sampling budgets to maximize
expensive-to-evaluate functions. One-step-lookahead policies are often used, but
computing optimal multi-step-lookahead policies remains a challenge. We
consider a specialized Bayesian optimization problem: finding the superlevel set of
an expensive one-dimensional function, with a Markov process prior. We
compute the Bayes-optimal sampling policy efficiently, and characterize the
suboptimality of one-step lookahead.
3 - Alleviating Competitive Imbalances in NFL Schedules:
An Integer Programming Approach
Kyle Cunningham, Northeastern University, Healthcare Systems
Engineering Institute, Boston, MA, United States of America,
, Murat Kurt, Niraj Pandey,
Mark Karwan
While the NFL uses complex rules in scheduling its games, NFL schedules are not
robust in creating a consistent competitive appeal. We propose a two-stage MILP
approach to reduce competitive disadvantages in schedules arising from various
sources including rest differentials due to bye-weeks and Thursday games, long
streaks of road games, and short-week travel. Our results for the 2012-2015
seasons indicate that our approach can substantially improve NFL schedules in
various fairness metrics.
4 - Robust Multi-Objective Clustering
Andy Zheng, Northwestern University, 1501 Leavenworth,
San Francisco, CA, 94109, United States of America,
We propose a multi-objective method that leverages robust optimization for
hierarchical clustering (rMOC). rMOC chooses clusters based on a weighted sum
of data-intrinsic objective functions, determining a threshold that is most robust
to uncertainties in these weights. We compare this method to the reference
methods of $K$-means and Gaussian mixture models. In terms of misassignment
rate, rMOC outperforms both other methods on several benchmark datasets.
5 - Optimal Resource Allocation in Breast Cancer Screening with
Different Risk Groups
Magdalena Romero, Universidad Adolfo Ibañez, Santiago,
Santiago, Chile, , Qingxia Kong,
Susana Mondschein
This paper investigates how many lives can be saved from breast cancer death
through optimal allocation of limited resources in public health. We build a two-
stage stochastic dynamic programming model to optimally allocate a limited
number of mammograms, among women with different risk levels in breast
cancer, which is applied to the case in Chile. We find that simply through dividing
women into 3 risk groups, we can save 88 lives per 100,000 women, compared to
the current practice in Chile.
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27-Room 404, Marriott
MCDM Methods and Applications
Sponsor: Multiple Criteria Decision Making
Sponsored Session
Chair: Luiz Autran Gomes, Ibmec/RJ. Rio de Janeiro – RJ, Brazil,
1 - Multi-criteria Evaluation of Sustainable Manufacturing
Jian-bo Yang, Professor Of Decision And System Sciences,
Manchester Business School, The University of Manchester,
Manchester, M15 6PB, United Kingdom,
Panitas Sureeyatanapas
Sustainable manufacturing becomes increasingly important. The paper explains
how a manufacturer can maintain its business and operations in long term by
combining green manufacturing, corporate social responsibility and green supply
chain. It then focuses on discussing how criteria and indicators for evaluating
progress to sustainable manufacturing can be established. A case study for
evaluating sustainability performance in the sugar manufacturing industry of a
developing country is discussed.
2 - Recent Development and Applications of Evidential Reasoning
Approach for Decision Making
Dong-ling Xu, Professor Of Decision Science And Systems,
Manchester Business School, The University of Manchester,
Manchester, United Kingdom, , Jian-bo Yang
We report the recently discovered relationship between Bayesian inference and
the Evidential Reasoning approach for multiple criteria decision making under
uncertainty. It is significant because it opens up new research avenues in many
fields such as the extension of Bayesian inference with imperfect probability
information which may not be fully reliable and the enhancement of evidence
and random set theories. A few applications are reported with a focus on
extracting evidence from big data.
3 - The Primary Aluminum Industry as a Complex Adaptive System
David Olson, Professor, University of Nebraska Lincoln, CBA 256,
Lincoln, NE, 68588-0491, United States of America,
Supply chains are critically important elements of global business, involving high
levels of interdependence. Supply chains have been suggested to be complex
adaptive systems. Supply chains usually emerge rather than result from the
purposeful design of a single controlling entity. This paper presents the global
primary aluminum industry viewed from the perspective of complex adaptive
systems. Unintended consequences of actor decisions in this industry in the past
forty years.
4 - MCDM Methods Inspired by Prospect Theory
Luiz Autran Gomes, Ibmec/RJ. Rio de Janeiro – RJ, Brazil,
This paper reviews attempts to develop discrete MCDM methods inspired by
Kahneman and Tversky’s prospect theory. After going through the first steps
making use of linear prospect theory the essentials of the TODIM method and
extensions are presented. The paper closes with outlining how qualitative
methods of MCDM such as DEX or Verbal Decision Analysis can be combined
with TODIM-based methods in order to approach complex decision making
problems.
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