2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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Application Of Multicriteria Methodology For Decision Aid In The
Formation Of A Projects Portfolio
Marcos Santos, Liutenant Commander, Brazilian Navy, Arsenal da
Marinha no Rio de Janeiro (AMRJ), Rua da Ponte S/N, Ilha da
Cobras, Centro, Rio de Janeiro, 20091000, Brazil,
Hudson Souza,
Fabrício Costa Dias, Ernesto Rademaker Martins,
Marcone Freitas Reis
Decide correctly is a constant challenge faced by man since the beginning of time.
Among the numerous multicriteria methods of decision support, it was used the
Analytic Hierarchy Process (AHP), which, as a compensatory method, it seems
appropriate to solve such problems. The AHP was one of the first methods
developed by the American School, one of the most used methods in the world.
This paper aims to propose through AHP, a methodology for constitution the
portfolio of IT projects of a non-profit company. Based on this interview it was
possible to raise the projects evaluation criteria as well as the preference of the
decision maker.
Pro Bono Analytics - Informs Volunteers Create Societal Impact
With Applied Analytics
Rina R Schneur, ARCKS, Lexington, MA, 02420, United States,
, Michael P Johnson
Pro Bono Analytics was established by INFORMS in 2015, in the tradition of
other disciplines’ efforts to utilize specialized skills and knowledge to generate
social impact. Pro Bono Analytics’ goal is to provide analytics technical support
for nonprofit organizations without the capacity and/or resources to perform data
analysis related tasks on their own. This poster presentation will provide
knowledge about why INFORMS members should consider volunteering for Pro
Bono Analytics, how this initiative works, and what promising current and
recently-completed engagements look like.
MLB And Regression Analysis. Predictions For The 2016
Chicago Cubs And White Sox
Kurt J Schuepfer, Graduate Researcher, Miami University,
Oxford, OH, 45056, United States,
Logistic and linear regression models were built to predict outcomes for the 2016
Chicago Cubs and White Sox. The models in concert predicted overall wins, runs
scored, runs allowed, and finally the predicted playoff status for each team.
Future directions in web scraping and model building are discussed.
Fuzzy-logic / Dempster - Shafer Based Information Fusion
Formulism For Land-marine Decision Analysis
Nicholas V Scott, Spectral Scientist/Physical Oceanographer,
Riverside Research, 2640 Hibiscus Way, Beavercreek, OH, 45431,
United States,
A land-marine problem is heuristically addressed using a fuzzy logic/Dempster-
Shafer based information fusion formulism which demonstrates the efficacy of
such tools as aids in optimal decision making. The initial computational segment
contains a five component feature extraction system which provides the inputs to
a fuzzy logic inference system. Multiple human assessments, which emanate from
the use of the inference system and ancillary intelligence, are then amalgamated
using Dempster-Shafer evidential theory. A probabilistic assessment of
environmental state is provided finally allowing for decisions in which
information ignorance and data uncertainty are taken into account.
Reducing Social Risks In The Supply Chain: An Examination Of
S&P 500 Companies
Rose Sebastianelli, Professor, University of Scranton,
Brennan Hall 423, Scranton, PA, 18510, United States,
, Nabil Tamimi
Approximately half of S&P 500 companies report implementing initiatives to
reduce social risks in the supply chain. Based on Bloomberg data, these S&P 500
companies are compared to those without such initiatives in terms of firm
characteristics (e.g., size, industry sector), related policies (e.g., child labor, human
rights, environmental) and profitability (e.g., return on assets).
Optimal Balanced Sample Selection For Causal Inference Using
Machine Learning
Dhruv Sharma, Graduate Student, George Washington University,
Washington, DC, 20429, United States,
With the availability of observational survey data and big data the ability to
sample accurately to determine causal effects beyond correlational studies is
important. This paper investigates machine learning supervised ensemble
classification Area Under the Curve (AUC) measure, for optimization of balanced
sample selection. Synthetic data sets and actual experimental data are used to
compare results of existing optimization metrics.
Adaptive Sampling Trust Region Algorithms For Derivative Free
Simulation Optimization
Sara Shashaani, Purdue University, 782 N Commodores Ln.,
Lafayette, IN, 47909, United States,
Raghu Pasupathy
We develop derivative free algorithms for optimization contexts where the
objective function is observable only through a stochastic simulation. The
algorithms we develop follows the trust-region framework where a local model is
constructed, optimized, and updated as the iterates evolve through the search
space. The salient feature of our algorithms is the incorporation of adaptive
sampling to keep the quality of the local model in lock step with the trust-region
radius, in a bid to ensure optimal convergence rates.
Ruled Based Prediction Analysis For 30-days Neurological
Recovery Status Post Stand Assisted Treatment Of
Brain Aneurysm
Karmel Shehadeh, PhD Student, University of Michigan,
1205 Beal Avenue, Ann Arbor, MI, 48109, United States,
Chun An-Chou
Recently, it has been observed that stroke patients could recover with
asymptomatic outcome in a short period with use of stent-assisted coiling (SAC)
treatment. We employed a rule-based decision model to identify key rules that are
used for predicting the clinical outcomes post 30-Days of SAC treatment. A 95%
and 75% prediction accuracy were obtained for a cohort of 65 training and 21
validation patients, respectively.
The Impact Of Social Feedback On Reviewers’ Review Decisions
Wenqi Shen, Virginia Tech, Blacksburg, VA, United States,
, Yan Liu
In this paper, we empirically examine how social incentives, namely online
reputation and social feedback which reflects peer recognition and attention,
affect reviewers’ review decisions. We develop a state-space model which
captures the dynamics of reviewers’ incentives as influenced by both online
reputation and social feedback.
Quay Crane Scheduling Problem With Considering Tidal Impact
And Fuel Consumption
Yu Shucheng, doctor, Shanghai university, Shang Da Road 99,
Shanghai 200444, China, Shanghai, 200444, China,
This study investigates a quay crane scheduling problem with considering the
impact of tides in a port and fuel consumptions of ships. A mixed-integer
nonlinear programming model is proposed. Some nonlinear parts in the model
are linearized by approximation approaches. For solving the proposed model in
large-scale problem instances, both a local branching based solution method and a
particle swarm optimization based solution method are developed. Numerical
experiments with some real-world like cases are conducted to validate the
effectiveness of the proposed model and the efficiency of the proposed solution
methods.
A Dynamic Programming Approach To Solve Bi-level
Programming Problem With Fuzzy Rule-base Constraints
Vishnu Pratap Singh, Research Scholar, Indian Institute of
Technology-Kharagpur, Department of Mathematics, Kharagpur,
WB, 721302, India,
In this work, A bi-level programming problem has been considered where the
functional relationship between decision variables and the objective functions of
leader and follower are not completely known to us. So a bi-level programming
problem with fuzzy rule-base constraints has been developed. A dynamic
programming approach with appropriate fuzzy reasoning scheme is used to
determine the crisp functional relationship between the objective functions and
the decision variables. Thus a bi-level programming problem is formulated from
the original fuzzy rule-based to the conventional bi-level programming problem.
Using Discrete Event Simulation To Improve Acute Stroke Care
Quality Measurement
Lina Song, PhD Candidate, Harvard University,
14 Story Street, 4th floor, Cambridge, MA, 02138, United States,
Time from stroke onset to the administration of tissue plasminogen activator
(tPA) is an important acute stroke care performance measure, but it should be
adjusted for the operational characteristics of hospitals to avoid setting unrealistic
benchmarks for smaller hospitals. We developed a discrete event simulation
model to compare the time-to-tPA among four types of hospitals with varying
stroke-related resources. Stroke patients arrive at an emergency department (ED)
according to a Poisson process and navigates through the system. According to the
model, larger comprehensive stroke centers can achieve better performance on
time-to-tPA measures compared to non-stroke centers.
POSTER SESSION