Informs 2015

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INFORMS Philadelphia – 2015
71
SB17
17-Franklin 7, Marriott
Networks Robustness and Vulnerability Analysis
Sponsor: Optimization/Network Optimization
Sponsored Session
Chair: Foad Mahdavi Pajouh, Assistant Professor, University of
Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA, 02125,
United States of America,
1 - On Imposing Connectivity Constraints in Integer Programs
Austin Buchanan, Oklahoma State University, 322 Engineering
North, Stillwater, OK, 74078, United States of America,
, Sergiy Butenko, Yiming Wang
In many network applications, one searches for a connected subset of vertices
that exhibits other desirable properties. To this end, we study the connected
subgraph polytope of a graph, which is the convex hull of subsets of vertices that
induce a connected subgraph. We investigate two classes of valid inequalities—-
separator inequalities and indegree inequalities—-and show when they induce
(all nontrivial) facets. We also consider extended formulations, lifting, and
separation.
2 - On Biconnected and Fragile Subgraphs of Low Diameter
Oleksandra Yezerska, Texas A&M University, 3131 TAMU, College
Station, TX, 77843, United States of America, ,
Sergiy Butenko, Foad Mahdavi Pajouh
An s-club is a subset of vertices inducing a subgraph with a diameter of at most s.
It is commonly used to characterize network clusters in applications for which
easy reachability between group members is of high importance. In this paper, we
study two special cases of the 2-club model – a biconnected 2-club, and a fragile
(not biconnected) 2-club, respectively. By investigating certain properties of both
models, we develop exact algorithms for their corresponding optimization
problems.
3 - Integer Programming Formulations for Solving the Minimum Edge
Blocker Spanning Tree Problem
Jose Walteros, University at Buffalo, 342 Bell Hall, Buffalo, NY,
United States of America, , Ningji Wei,
Foad Mahdavi Pajouh
The minimum edge blocker spanning tree problem consists of removing a
minimum number of edges in a weighted graph so that the weight of any
spanning tree in the remaining graph is at least r. We study the convex hull of
feasible solutions and identify facet-inducing inequalities for this polytope. We
develop an exact algorithm that solves our formulation via branch-and-cut.
Finally, we provide the computational results obtained after solving a set of
randomly generated and real-life instances.
SB18
18-Franklin 8, Marriott
Joint Session Modeling Methodologies/Big Data:
Large-Scale Data Analytics and Applications
Cluster: Modeling and Methodologies in Big Data
Invited Session
Chair: Chou-An Chou, Binghamton University, 4400 Vestal Parkway,
Vestal, United States of America,
Co-Chair: Anas Hourani, Binghamton University, 4400 Vestal Parkway,
Vestal, United States of America,
1 - Understanding Patterns or Relations of the Terrorist Attacks in Big
Data to Prevent Future Threats
Salih Tutun, Binghamton University, 4400 Vestal Parkway East,
Binghamton, NY, 13902, United States of America,
Chou-An Chou
This research interests value in 5Vs that is the useful results about terrorist attacks
(incidents) to analyze the terrorist activity patterns or relations, to predict their
future moves, and finally to prevent potential terrorist attacks. We focused on
understanding of why incident succeed or fail, duration of incident extended, and
doubt as to whether the incident is an act of terrorism. The results will be very
useful for law enforcement agencies to propose reactive strategies.
2 - Machine Learning Based Robust Optimization with Application to
Healthcare Treatment Recommendations
Sung Hoon Chung, Binghamton University, P.O. Box 6000,
Binghamton, NY, United States of America,
Yinglei Li
When making decisions, one may use robust optimization (RO) to reduce the
impact of uncertainty modeled from the past data. In RO, uncertain parameters
are generally assumed to be within a convex set, within which decision makers
want to protect the system against the worst case. We propose a machine learning
approach to design such uncertainty sets, and discuss the application of machine
learning based robust optimization to healthcare treatment recommendations.
3 - Frequency-based Rule Classification Algorithm with Big Data
Anas Hourani, Binghamton University, 4400 Vestal Parkway,
Vestal, NY, United States of America, ,
Chou-An Chou
Frequency-based rule classification algorithm is a simple, fast and accurate
associative algorithm. In this study, we are going to introduce an improvement on
the FRC algorithm to work Hadoop system. Then several comparisons are made
between the conventional FRC and FRC on Hadoop to demonstrate the efficiency
of the proposed algorithm with big data.
SB19
19-Franklin 9, Marriott
Computational Optimization for Applied Problems I
Sponsor: Computing Society
Sponsored Session
Chair: David Morrison, Inverse Limit, 255 McDowell Lane Unit A, W
Sacramento, CA, 95605, United States of America,
1 - Enumeration and Bounding Arguments for Infrastructure
Resilience Analysis
W. Matthew Carlyle, Naval Postgraduate School,
David Alderson
We propose a functional definition of infrastructure resilience based on attacker-
defender models and using a parametric analysis of attacker capability. In general,
this parametric analysis requires enumeration of a potentially enormous number
of optimization problems. In this talk, we present a computational technique that
uses bounding arguments to significantly limit the enumeration while still
providing useful measures of infrastructure resilience. We illustrate with a real
case study.
2 - Computational Study of Stabilization Methods in
Crew Pairing Problems
Jiadong Wang, Senior Operations Research Developer, Sabre,
3150 Sabre Drive, 76092, United States of America,
, Xiaodong Luo
Column generation has been successfully applied to solve large-scale optimization
problems. In this talk, we review the various stabilization methods by revisiting
primal-dual subproblem approach. Experiments on large set-partitioning
instances from crew pairing problem in Sabre provide insight on the effectiveness
of various stabilization methods.
3 - An Implicit Hitting-set Solution Method for the Sensor
Location Problem
David Morrison, Inverse Limit, 255 McDowell Lane Unit A,
W Sacramento, CA, 95605, United States of America,
, Paul Rubin
Given a two-way directed network, the sensor location problem seeks to find the
minimal number of vertices that must be monitored so that flow over the entire
network can be determined, given prior assumptions on flow patterns. We reduce
the problem to an implicit hitting set problem and use Benders’ decomposition to
solve the problem. The decomposition subproblem is a novel network flow
problem called the incremental flow problem, for which we present several
solution techniques.
SB19