2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Some Thoughts On Implementing Linear Optimization Algorithms
Tamás Terlaky, Lehigh University,
We discuss some aspects of implementing simplex and interior point algorithms
for LO. Among others, we discuss the role of duality in algorithms design, in
preprocessing, in choosing the most promising algorithm, in choosing if the given
problem should be considered as primal or dual. The provocative question
naturally arises: Do we indeed have both primal and dual simplex algorithms?
Another question is if an optimal basis is always needed? Then, the algorithmic
and practical consequences of choosing either crossover or optimal basis
identification procedure are discussed.
3 - An Advanced Starting Basis For The Simplex Algorithm
Nikolaos Ploskas, Carnegie Mellon University, Pittsburgh, PA,
United States, Nikolaos Samaras,
Nikolaos Sahinidis
The computation of a starting basis for the simplex algorithm is of great
importance. We propose six algorithms for constructing an initial basis using
various ordering methods in order to generate a nearly-triangular and sparse
initial basis. We give the initial bases as input to the CPLEX solver and compare
the performance of the primal and dual simplex algorithm using the proposed
algorithms against CPLEX advanced starting basis and crash procedures. The best
algorithm results in 95% and 87% reduction of the execution time of the primal
and the dual simplex algorithm, respectively.
4 - A Planning Approach And a Decision Support System For Wine
Bottling Operations
Alfredo R. Squadritto, Pontificia Universidad Católica de
Valparaíso, School of Industrial Engineering, Valparaíso, Chile,
, Ernesto Vásquez, Ricardo A. Gatica,
Sergio G. Flores, Ricardo Gatica
We will describe a MIP-heuristic based decision support system (DSS) for
production planning and scheduling at wine bottling plants. The production
system involves several complicating features such as the coexistence of items
made-to-order with items made-to-inventory, a complex structure, several
constraints on wine supply, and a variety of company’s policies. The DSS offers
tools for easily modifying and analyzing the production schedule using a friendly
graphic user interface. It’s been implemented in 3 plants. The largest plant
handles about 2400 SKUs, 400 intermediate products, and 3500 types of raw
material, and it processes, on average, 250 production orders per week on 2
parallel production lines.
TA14
104D-MCC
Data Mining in Genetics and Genomics
Sponsored: Data Mining
Sponsored Session
Chair: Michael M Hoffman, Princess Margaret Cancer
Centre/University of Toronto, Toronto Medical Discovery Tower
11-311, 101 College Street, Toronto, ON, M5G 1L7, Canada,
1 - Modeling Methyl-sensitive Transcription Factor Motifs With An
Expanded Epigenetic Alphabet
Michael M Hoffman, Scientist, Princess Margaret Cancer Centre,
Toronto Medical Discovery Tower 11-311, 101 College St, Toronto,
ON, M5G 1L7, Canada,
Michael M Hoffman, Scientist, University of Toronto, Toronto
Medical Discovery Tower 15-701, 101 College St, Toronto, ON,
M5G 1L7, Canada,
To understand the effect of DNA methylation on gene regulation, we developed
methods to discover motifs and identify TF binding sites (TFBS) in DNA with
covalent modifications. Our models expand the standard A/C/G/T alphabet,
adding m for 5-methylcytosine. We adapted the position weight matrix model of
TFBS affinity to an expanded alphabet. Using ChIP-seq data from Mouse
ENCODE and others, we identified modification-sensitive cis-regulatory modules.
We elucidated various known methylation binding preferences, including the
methylation preferences of ZFP57, C/EBP , and c-Myc.
2 - Data, Informatics, And Analytical Challenges In
Genomic Medicine
Elizabeth A. Worthey, Faculty Investigator & Director,
HudsonAlpha Institute for Biotechnology, 601 Genome Way,
Huntsville, AL, United States,
Application of Next Generation Sequencing has transformed genomic research. It
is also transforming medicine through use as a molecular diagnostic test in both
rare disease and oncology. It can also identify much (though not all) of the
variation associated with more common and polygenic disease. To support such
clinical and translational advances investment in computational applications,
hardware, and methodologies has been necessary. Informatics environments,
tools, and processes supporting medical genomics have been developed or refined
and validated for clinical use. This talk will highlight various successes and will
discuss potential solutions to some of the challenges that remain.
3 - Machine Learning For Predicting Vaccine Immunity
Eva K. Lee, Georgia Institute of Technology,
This work is joint with Emory Vaccine Center and CDC. The ability to better
predict how different individuals will respond to vaccination and to understand
what best protects individuals from infection greatly facilitates developing next-
generation vaccines. It facilitates both the rapid design and evaluation of new and
emerging vaccines as well as identifies individuals unlikely to be protected by
vaccine. We describe a general-purpose machine learning framework, DAMIP, for
discovering gene signatures that can predict vaccine immunity and efficacy.
TA15
104E-MCC
Academic Job Search Panel
Invited: INFORMS Career Center
Invited Session
Chair: Wedad Jasmine Elmaghraby, University of Maryland, College
Park, MD, United States,
1 - Academic Job Search Panel
Wedad Jasmine Elmaghraby, University of Maryland,
The panel will discuss the academic interview process and do’s and don’ts
associated with the job search. In addition to comments by current and former
search chairs, time will be provided for questions and answers.
2 - Panelist
Volodymyr O Babich, Georgetown University,
3 - Panelist
Candace Arai Yano, University of California-Berkeley,
4 - Panelist
Brian Tomlin, Tuck School of Business,
5 - Panelist
Ken Moon, Univeristy of Pennsylvania, Wharton School,
Philadelphia, PA, 19104, United States,
6 - Panelist
Ken Moon, Univeristy of Pennsylvania, Philadelphia, PA, United
States,
TA16
105A-MCC
Energy Systems
Sponsored: Optimization, Optimization Under Uncertainty
Sponsored Session
Chair: Bruno Fanzeres, Visiting PhD Student, Georgia Institute of
Technology, 755 Ferst Drive, NW, Atlanta, GA, 30332, United States,
1 - The Information-collecting Vehicle Routing Problem For
Emergency Storm Response
Lina Al-Kanj, Postdoctoral Research Associate, Princeton
University, Olden Street, Sherrerd Hall, room 119, Princeton, NJ,
08544, United States, , Warren B Powell”
This talk presents a new policy that routes a utility truck to restore outages in the
power grid using trouble calls and the truck’s route as a mechanism for collecting
information to create beliefs about outages. This means that routing decisions
change our belief about the network, creating the first stochastic vehicle routing
problem that explicitly models information collection. The problem is formulated
as a sequential stochastic optimization program. Then, a stochastic lookahead
policy is presented that uses Monte Carlo tree search (MCTS) to produce a
practical policy that is asymptotically optimal. Simulation results show that the
developed policy has a close-to-optimal performance.
TA16