2016 INFORMS Annual Meeting Program

178 / 561

INFORMS Nashville – 2016
178
Non-convex Problems For Long-term Hydropower Scheduling
Martin N. Hjelmeland, PhD Candidate, Norwegian University of
Science and Technology, O.S. Bragstads plass 2E, Trondheim, 7491,
Norway,
With its large operational flexibility, hydropower may provide spinning reserves
at a low cost. With the introduction of a market for providing capacity in the
scheduling, the unit commitment problem in the stochastic multistage
hydropower scheduling problem becomes essential. This is mainly due to the
minimum generation restriction for a hydropower station that should be enforced
to ensure a practicable dispatch. This work will focus on methods to solve these
kinds of problems, and evaluate its importance for the hydropower scheduling
problem. Methods that will be applied are Stochastic Dynamic Programming
(SDP) and Stochastic Dual Dynamic Programming (SDDP) with possible
extensions.
A Two-echelon Decomposition Method On Fresh Product
Distribution Problem
hongtao HU, Associate Professor, Shanghai Maritime University,
Room101, No 96, 555 Guzong Road, Shanghai, 201306, China,
Refrigerator cars are widely used for fresh product distribution. The energy
consumption of these vehicles is sensitive to the environment temperature. To
reduce operation costs of third-party transportation providers (TPTP), the
refrigerator car scheduling problem is addressed in this research. A time-
dependent mixed-integer programming model is established to reduce total
operation costs. An adaptive heuristic method is proposed by combining the
variable neighborhood search and particle swarm optimization.Numerical
experiments are conducted to demonstrate the effectiveness of the proposed time-
dependent decision model.
Modeling Capacity Planning Projects In The Automotive Industry
Using A Markov Decision Process
Paul Jana, Technische Universität München (TUM), Arcisstr 21,
Munich, 80333, Germany, , Martin Grunow
To support automotive OEMs in time-phased decision making during capacity
planning projects connected to new vehicle introductions, we present a dynamic
programming approach based on a Markov Decision Process. We employ
Bayesian updating to anticipate forecast updates and adapt standard risk
measures. Our methodology is superior over alternative stochastic approaches.
Assessment Of Clustering Algorithms Based On A
Data Mining Technique
Youngseon Jeong, Assistant Professor, Chonnam National
University, 77 Yongbong-ro, Buk-gu, Gwangju, 61186, Korea,
Republic of,
This research presents a novel performance assessment of clustering compactness
based on one-class classification algorithms. The proposed method evaluates the
compactness of each cluster by using support vector data description (SVDD) and
Bayesian support vector data description (BSVDD), which is robust to arbitrary
shapes of a clustering. In addition, the proposed approach can accurately evaluate
a clustering compactness in kernel space. The experimental results show that the
proposed method can evaluate the accurate compactness for arbitrary shapes of a
clustering.
The Status, Development Potential Prediction And Policy
Recommendations Of New Energy In China
HongDian Jiang, Master Degree Candidate, China University of
Petroleum, No.18, Fuxue Road, Changping District,, Beijing,
102249, China, , KangYin Dong, RenJin Sun
By combining the GM (1, 1) grey model with BP neural network model and
establishing a combined Grey-BP modelling tool, future the development
potential of China’s new energy is forecasted. In addition, the ranking of the
development potential for the different new energy fuel types is performed, from
both the development scale and growth rate perspective. According to our
estimation, China’s total new energy consumption will increase to 690.5 Mtoe in
2020, accounting for 19.7% of the domestic energy need. Besides, according to
the rank results, nuclear and solar energy will be considered as future oriented
composition of the new energy, as well as hydropower considered as the key
element in China.
Predictive Maintenance From Analysis Of Airplane Sensor Data
Ruiwei Jiang, Data Scientist, Boeing Vancouver,
1146 Homer Street, Vancouver, BC, V6B 2X6, Canada,
Phillip Mah,
Dawen Nozdryn-Plotnicki, Benji Shieh, Hubert Duan
Unscheduled maintenance drives 10% of the annual operational cost to airlines
worldwide. Predictive Maintenance could reduce those costs, particularly when
synchronized with airline’s operations. By using engineering expertise, statistics
and machine learning on aircraft sensor and fault data, as well as analysis of an
airline’s flight and maintenance schedule, we detect impending issues on the
aircraft and suggest maintenance tasks in accordance with the prediction and an
airline’s working rhythm. These predictive maintenance tasks will increase
reliability and reduce unscheduled maintenance.
Eliminating Preventable Motor Vehicle Accidents Through
Simulation Scenarios Of Vehicle Modifications
Sahar Khamsehi, Prospective Phd Student/Practical trainer,
Binghamton University, 4400 Vestal Pkwy E,, Binghamton, NY,
13902, United States, ,
Constance Dwyer
Facilitator for express work out initiative to determine ways to improve driving
performance in enterprise divisions. Developed SharePoint team site. Supported
effort with Jack 7.1 simulation tool model to evaluate statistical significance of
vehicle improvements with extended graduate student team resulted in optimized
scenarios where the certain vehicle modification resulted in an improved visibility
measured by simulation software. Consequently, the study led to proved solutions
in order to eliminate preventable accidents.
Building A Luxury Products Evaluation Model
Youn Sung Kim, Professor, Inha University, 253 Yonghyun-Dong,
Nam-Gu, Incheon, 402-751, Korea, Republic of,
Considering the market size and history of luxury product, it is very surprising
that there is no holistic model to evaluate luxury product market. In this study
we try to propose comprehensive perspectives and policy considerations on
existing research about the evaluation of luxury products in terms of both
product characteristics and value. It has been focused on the brand value. Even
though it needs the verification process of proposed evaluation model, this
research indicates that it considers the new viewpoint of the evaluation of luxury
products. Therefore, the following study will be substantiated based on case study
and focused expert group interview.
Strengthening Keystroke Dynamics Based User Authentication
Based On User Adaptive Feature Construction For One Class
Classification
Junhong Kim, Korea University, Seoul, Korea, Republic of,
, Haedong Kim, Boseop Kim
This study present a new KDA method based on one-class classification(OCC)
with user-adaptive feature construction scheme. Since users have their own
typing patterns, the average typing speeds of digraphs are also different between
users. Hence, we construct eight features by considering the rank of the typing
speed of digraph for four typing speed measures for each user. A total of five OCC
is then trained for a valid user is applied to classify a new keystroke data. We
collected more than 10,000 keystrokes from 150 participant. Based on the
experiment with 25 combinations of training and test keystroke size, the
proposed model yielded lower EER than the conventional feature construction
method.
Evaluating Information Quality For News Articles Based On
Topic Modeling
Hyungseok Kim, Korea University, Anam-dong Seongbuk-gu,
Korea University, School of Industrial Management Engineering,
Seoul, 163-713, Korea, Republic of, ,
Kim Boseop
We propose two topic model-based information quality evaluation measures for
news articles: Relevance and Uniqueness. Relevance of an article is assessed by
the weighted sum of the average per-topic posterior probabilities of user-provided
keywords and the per-document topic distributions. Uniqueness of an article is
assessed by the score of a novelty detection algorithm based on per-document
topic distributions. The proposed model is applied to Korean economic news
articles and qualitatively verified by domain experts.
Why Firms Disappear: Bankruptcy In The Thoroughbred Horse
Industry’s Social Network
Angela King, Chapman University CMB: 1406, 1 University Drive,
Orange, CA, 92866, United States,
Darcy Fudge Kamal, Cristina Nistor
I look at how social influence in the Thoroughbred Horse Industry network can
influence the value for goods at auction. Firms going through a bankruptcy are
forced to sell off their goods while the network of social connections is affected by
the news of their impending bankruptcy. I analyze whether the network takes
into account that these firms will not exist in the future. I use clustering analysis
to find network patterns in which account for the differences in network
structures related to the node deletion from 2010-2014.
Nondestructive Quality Inspection Using Piezoelectric
Transducers Affixed To A Fixture
Tomilayo Komolafe, PhD Candidate, Virginia Polytechnic Institute
and State University, 1145 Perry Street, Blacksburg, VA, 24061,
United States,
Product change detection is of utmost importance in any manufacturing
environment and it is one of the major goals in quality control practices. Some
changes could be due to a malicious cyber-physical attack which is inherently
very difficult to detect by traditional inspection schemes. This study proposes to
use piezoelectric transducers (PZT) affixed to a fixture to perform nondestructive
quality inspection. Mechanical impedance information is obtained through
exciting the PZT bonded to the fixture-part combination and signal processing is
used to identify presence of an alteration.
POSTER SESSION