Informs 2015

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INFORMS Philadelphia – 2015
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10 - Advanced Decision-making Procedures in Massive Failure
Data Classification
Keivan Sadeghzadeh, Northeastern University,
27 Payne Rd, Newton, MA, 02461, United States of America,
In many professional areas, management decision-making process is based on the
type and size of data where data classification is a necessary procedure. Massive
amount of data in high-dimensions are increasingly accessible from various
sources and it has become more difficult to process the streaming data in
traditional application approaches. This poster presents advanced procedures to
analyze high-dimensional failure data in order to facilitate decision-making
through data classification.
11 - Exploring Residents Attitude Towards Solar Photovoltaic
System Adoption in China
Yaqin Sun, Drexel University, 38 Clarence Avenue, Bridgewater,
PA, United States of America, Xiangrong Liu
The research aimed to identify the drivers and dynamics that most encourage
Chinese customers to install solar PV systems (SPS) in their residential buildings.
A survey was designed and conducted among Chinese residents. The first hand
data indicated the importance of increasing awareness of SPS among potential
consumers. This research also assessed the impacts of gender on their knowledge
of, concerns, and attitudes towards PV adoption. However, no significant
difference among gender was found.
12 - Design of Financial Incentive Programs to Promote Net Zero
Energy Buildings
Alireza Ghalebani, University of South Florida, Tampa, FL,
United States of America, Tapas Das
Promoting net zero energy buildings (NZEB) is among key carbon emissions
reduction approaches in the U.S. and in the EU countries. We present a mixed
integer programming (MIP) model to aid determining the minimum thresholds of
financial incentives that would spur growth in NZEBs. Several combinations of
production tax credit and loan interest rates have been investigated for different
commercial buildings in Tampa, FL. The results indicate the threshold values of
the incentive program parameters.
13 - Multi-objective Scenario Discovery for Climate
Change Adaptation
Julie Shortridge, PhD Student, Johns Hopkins University, 3400 N.
Charles St., Ames Hall 317, Baltimore, MD, 21218, United States
of America, Seth Guikema
New methods for decision support under non-probabilistic uncertainty are
becoming increasingly popular in the climate change adaptation field. Scenario
discovery, as part of the robust decision making framework, uses machine
learning to identify multivariate scenarios where a plan or system will perform
poorly. In this work, we evaluate different methods for incorporating multiple
criteria into the scenario discovery process to assess whether the method used
impacts the scenarios identified.
14 - The Unit Commitment Model for Power Interruption Contracts
Lakshmi Palaparambil Dinesh, PhD Candidate, University of
Cincinnati, 221 Piedmont Avenue Apt. 21, Cincinnati, OH,
45219, United States of America, , Jeffrey
Camm
The term unit commitment implies which power generation units should be
turned on or off in a power plant . When the demand for power is high, power
could either be bought from the spot market or the customers could be
interrupted using a contract. The problem deals with choosing the right set of
customers for interruption using a technique called conjoint optimization and
hence reducing the overall costs for the supplier.
15 - Virtual Metrology for Copper Clad Laminate Manufacturing
Misuk Kim, Seoul National University, 39-339, Gwanak-ro,
Gwanak-gu, Seoul, Korea, Republic of,
Virtual metrology predicts wafer quality properties based on sensor values of the
equipment in semiconductor manufacturing. It reduces the cost associated with
physical metrology as well as identifies important equipment sensor values. We
applied it to copper clad laminate for printed circuit board with data from a
Korean manufacturer. We not only obtained prediction models with a high
accuracy, but also found a number of important, yet previously unknown to
engineers, equipment sensors.
16 - Goodness of Fittest for Multinomial Model with Clustered Data
Zhiheng Xie, PhD Candidate, University of Kentucky, Lexington,
KY, 40503, United States of America,
Discrete-time Markov chains have been used to analyze the transition of subjects
from intact cognition to dementia with transient states, and death as competing
risk. We proposed a modified chi-square test statistic which can deal with the
clustering effects for the multinomial assumption. We showed our new statistic
has a better type I error control when clustering effects presents. We apply the
test to the data from the Nun Study, a cohort of 461 participants.
17 - Discrete Event Dynamic Simulation for Modeling a Real Job
Shop System
Golshan Madraki, Ohio University, 15 Station St, Apt. F, Athens,
OH, 45701, United States of America,
A new approach for simulating a job shop system is introduced.The interarrival
time of jobs,processing time of machines,time between failures,repair time have
general distribution. Previous models consider these parameters deterministic or
exponentially distributed. we facilitate estimation of maximum production rate
where Buffers capacity,Number of machines in each shop,Number of Lift-truck
are efficient
18 - Optimization of Food Production (Ready-To-Eat Meat Sticks)
Rebecca Brusky, Data Science Student, University of Nebraska
Omaha, 3602 Lincoln Blvd, Omaha, NE, 68131,
United States of America, Betty Love
In the production of ready-to-eat meat sticks, the bottlenecks (dependencies)
need to be minimized and number of sticks produced needs to be maximized.
Dependent components include equipment flow constraints, smoke room
duration and cleaning downtime. The largest downtime factor is the required
four-hour cleaning when switching to a non-compatible flavor. This poster
documents how a six-flavor production line governed by a set of flavor ordering
rules and production demands can be optimized.
19 - Rethinking Principal Component Analysis in EEG Classification
Xiaoxia Li, North Dakota State University, 124 East Bison Court,
Fargo, ND, 58108-6050, United States of America,
Principal Component Analysis (PCA) is considered to be a powerful tool in
dimension reduction. However, it is worth thinking of the suitability of
application for EEG signal data. Two EEG datasets collected from alcoholic and
control groups were used to test the prediction accuracy before and after PCA
transformation with SVM and KNN methods. Based on the classification results,
we found that PCA is not valid in EEG signal processing. We also concern that
other factors might be confounding.
20 - Strategic Exclusive Supply Contract for Carbon Fiber Reinforced
Plastic in the Aviation Industry
Kenju Akai, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku,
Tokyo, Japan, Kazuma Sakamoto,
Nariaki Nishino, Kazuro Kageyama
We investigate the rationality of an exclusive supply contract for Carbon Fiber
Reinforced Plastic (CFRP) between Boeing and a Japanese CFRP supplier, Toray.
We build a mathematical model of the market for CFRP comprising Toray and the
oligopolistic market for aircraft, assuming Airbus, as Boeing’s rival. The subgame
perfect Nash equilibria show that both Boeing and Toray obtain the higher profits
rather than that in the Cournot Competition.
21 - Hand Motion Identification from Electroencephalography
Recordings using Recurrent Neural Network
Jinwon An, SNU, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742,
Seoul, Korea, Republic of,
Sungzoon Cho
Neurological disabled patients can be aided by brain-computer interface (BCI)
prosthetic devices. Grasp and lift tasks are basic actions that needs to be
implemented in those devices. In this study, grasp and lift tasks were analyzed by
using electroencephalography (EEG) recordings. Various recurrent neural
network models were used. It shows that EEG can identify hand motions such as
reaching, grasping, loading and retracting with high accuracy.
22 - On Optimization of Carbon Capture, Utilization, and Storage
Supply Chains under Uncertainty
Mahnaz Asghari, Virginia Tech, 1406 University City Blvd.,
Blacksburg, VA, 24060, United States of America,
, Hamed Shakouri Ganjavi
Carbon capture, utilization, and storage (CCUS) is a crucial technology to mitigate
climate change. Due to the high costs of the technology, a great deal of attention
has been focused on how the captured CO2 can be optimally utilized or stored.
We study optimizing CCUS supply chains under uncertain environment. In this
poster, we present an algorithm to generate a candidate network for CO2
transportation and a model for optimizing the utilization and storage of the
captured CO2 in CCUS systems.
23 - On Two-row Chvatal Gomory Cuts
Babak Badri Koohi, Doctoral Student, Virginia Tech,
1406 University City Blvd., Blacksburg, VA, 24060,
United States of America, Diego Moran
Chvatal-Gomory (CG) cuts are a very important class of cutting planes for solving
mixed-integer programs. CG cuts for a polyhedron P are obtained by computing
integer hulls of its 1-row relaxations. We study 2-row CG cuts, a generalization of
CG cuts that are obtained by computing integer hulls of 2-row relaxations of P. In
this poster, we present some basic properties of 2-row CG cuts and discuss their
relation to other well-known classes of cuts such as split cuts and (crooked) cross
cuts.
POSTER SESSION