2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Purchase Prediction Based On Multilevel Association Rule Mining
Xinxue Qu, College of Business, Iowa State University, Ames, IA,
50010, United States, Zhengrui Jiang
Recommender systems are one of the most widely deployed applications in E-
commerce. The goal of this study is to improve existing association-rule-based
methods to increase the quality of product recommendations. There are two
important factors in our method. First, due to the huge number of products in
stores, market basket data is often sparse. Second, competing products are often
highly substitutable, and consumers may be open to alternatives. The method we
propose infers the level of similarity/substitutability between pairs of products
from product category information. Experimental results show multilevel
association rules can lead to a higher accuracy of purchase predictions.
3 - Risk Information Disclosure And Project Success Rate
Yang Pan, University of Maryland,
Since information asymmetry between funders and creators is a critical issue in
crowdfunding platform, many policies are introduced to improve the information
transparency and make markets more efficient. One of the mechanisms is
mandatory disclosure imposed by platform. We aim to understand how disclosed
risk information has an effect on project outcomes. We study this question on a
popular crowdfunding site requiring project creators disclose potential risk
information about projects. We analyze the detail content of the disclosed risk
information with text mining techniques and test the association between self-
disclosed risk information and successful rate of crownfunding projects.
SD10
103C-MCC
INFORMS Prize
Invited: INFORMS Prize
Invited Session
Chair: Julia Morrison, Marriott International, Department 51/974.18,
Bethesda, MD, 20817, United States,
1 - 2016 INFORMS Prize Presentation by GeneralMotors
Michael Harbaugh, General Motors, Warren, MI, United States,
, Robert Inman, Peiling Wu-Smith,
Yilu Zhang
General Motors, 2016 INFORMS Prize Winner, will survey its sustained
application of analytics and operations research. Highlights will include
Vehicle Health Management: using advanced analytics to predict failure of certain
automotive systems before customers are affected, Optimizing New Vehicle
Inventory: determining first how much, and second what mix of vehicles to hold
in dealer inventory, andRevenue Management for Vehicle Content and
Packaging: leveraging customer preferences to package and price vehicle content
that will sell best.
SD11
104A-MCC
Network Optimization
Sponsored: Optimization, Network Optimization
Sponsored Session
Chair: Alexander Nikolaev, Assistant Professor, University at Buffalo,
312 Bell Hall, Buffalo, NY, 14260, United States,
1 - Optimal Seed Activation Scheduling For Influence Maximization
In Social Networks
Mohammadreza Samadi, Operations Research Consultant,
American Airlines, Fort Worth, TX, United States,
Alexander Nikolaev,
Rakesh Nagi
Influence maximization problem selects a set of influential nodes, called seeds, in
a social network to spread the influence over the network maximally. We critique
the basic assumption of influence maximization problem in the literature on
controlling cascades only through the early starters and present Seed Activation
Scheduling Problem (SASP) in two-level networks. The SASP is a sequential seed
selection problem that results in optimal budget allocation over the campaign
time horizon. The problem is modeled as a mixed-integer program for blogger-
centric marketing campaigns and an efficient heuristic algorithm is presented
using column generation method.
2 - From Local To Global Connections: A New Random Graph Model
To Explain The Structural Properties Of Real-world Networks
Rakesh Nagi, U of Illinois at Urbana-Champaign, Department of
Industrial & Enterprise Systems, 117 Transportation Building, MC-
238, Urbana, IL, 61801, United States,
Sushant Khopkar, Alexander Nikolaev
Online Social Network (OSN) data are hard to interpret. Many OSN users have
lots of connections, easily surpassing 150 - the Dunbar number. We present a
random graph formation model that explains social tie formation by bridging the
gap between the Watts-Strogatz and scale free networks. It shows how the
information about “talented” individuals may propagate from their friends
towards the masses, with a power law in degree emerging via the mechanism
fundamentally different from preferential attachment (PA): while PA assumes full
visibility, our model relies on local information exchanges. We report and
interpret the model parameter estimates for several real-world networks.
3 - Constrained Sparse Optimization For Tensor Based Modeling Of
Student Learning In Collaborative Environments
Alireza Farasat, University at Buffalo,
Educational systems have witnessed a substantial transition from traditional
educational methods mainly using text books, lectures, etc. to newly developed
systems which are artificial intelligent-based systems personally tailored to the
learners. In this study, a constrained sparse tensor-based factorization approach is
proposed for modeling of student learning in collaborative environments. The
main challenge of modeling students learning is the fact that learning occurs over
time therefor. We develop a probabilistic, constrained based approach to the
tenser factorization model which enables capturing the underlying dynamics of
students learning over time.
4 - Generalized Cascade Model And Seed Bounds For Disease
Spread In Social Networks
Arash Ghayoori, U of Illinois at Urbana-Champaign, Urbana, IL,
61801, United States, , Rakesh Nagi
In this talk, we introduce a new diffusion model for social networks, which
generalizes most of the previously introduced diffusion models. We establish its
relevance in disease spread (epidemiology) as well as viral marketing. An upper
bound on the size of the influential set (“seed” set of nodes that if become
infected, will eventually result in making the entire network becoming infected)
is also obtained for a special case of this model. We show this bound to be tight by
providing a simple algorithm that outputs an influential set with size nearly equal
to this upper bound.
SD12
104B-MCC
Convexification Techniques in Integer Programming
Sponsored: Optimization, Integer and Discrete Optimization
Sponsored Session
Chair: Sercan Yildiz, Carnegie Mellon University, 5000 Forbes Ave,
Pittsburgh, PA, 15213, United States,
1 - Sparse Pseudoinverses Via LP And SDP Relaxations
Of Moore-Penrose
Jon Lee, University of Michigan,
Pseudoinverses are ubiquitous tools for handling over- and under-determined
systems of equations. For computational efficiency and also in the context of
identifying Gaussian models having a sparse precision matrix, sparse
pseudoinverses are desirable. Recently, sparse left and right pseudoinverses were
introduced, using 1-norm minimization and linear programming. We introduce
new sparse pseudoinverses by developing tractable convex relaxations of the
wellknown Moore-Penrose properties. In the end, we have several new sparse
pseudoinverses that can be calculated via linear and semi-definite programming.
2 - Optimal Truss Topology Design By Mixed Integer
Conic Optimization
Tamas Terlaky, Lehigh University,
Mohammad Shahabsafa, Ali Mohammad-Nezhad, Luis F Zuluaga
We present novel models, including Mixed Integer Linear Optimization (MILO)
and Mixed Integer Second Order Cone Optimization (MISOCO) models, for Truss
Topology Design Optimization. We discuss how classes of non-convex models can
be reformulated as MILO and MISOCO models. We present our approach to solve
the MISOCO models through adding Disjunctive Conic Cuts in a BCC framework.
Additionally, we present an efficient line search method developed to solve the
original non-convex model. Preliminary computational results indicate the
effectiveness of our novel approaches.
SD12