2016 INFORMS Annual Meeting Program

477 / 561

INFORMS Nashville – 2016
477
WD68
Mockingbird 4- Omni
Complex Process Modeling, Monitoring
and Optimization
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Youngseon Jeong, Chonnam National University, Korea,
Republic of,
Co-Chair: Myong K Jeong, Rutgers University, 96 Frelinghuysen Road,
Piscataway, NJ, 08854, United States,
1 - A Predictive Modeling Of Pump Breakdown For Semiconductor
Fabrication Based On Sensor Data
Kil Soo Kim, Principal Engeneer, Samsung Electronics,
SamsungJeonja-ro, Hwasung, Gyeonggi-do, 18448, Korea,
Republic of, Chanhwi Jung, Solmi Park,
Hyung-Seok Kang, Dongkyu Jeon, Seung Hoon Tong
We present a method for predictive monitoring of pump breakdown failure which
may result in wafer scrap or unnecessary main chamber stop loss based on
secondly collected sensor data. The sensor data set that were collected massively
during several months from pumps contains useful information to monitor and
predict the condition of pump connected with main chamber. We developed the
monitoring statistics and predictive model which can estimate the remaining
useful life. The real-life case study is presented to illustrate our proposed
procedures.
2 - Application For Modeling And Optimization Of A Crucial
Parameter Identification
Shreya Gupta, University of Texas at Austin, Austin, TX,
We are working with Samsung Semiconductor on building a statistical
optimization model that identifies equipment and parameters to rank best and
worst routes for the purpose of scheduling and testing modified recipes. We are
also employing data mining and statistical techniques to identify control spec to
improve process capability and yield for semiconductor manufacturing. Finally,
we are developing a prototype software to demonstrate the use and value of the
proposed analysis and algorithms.
3 - Phase I Analysis Of Nonlinear Profiles Via Gaussian
Process Models
Yang Zhang, Tianjin University of Commerce, No. 409, Guangrong
Rd., Tianjin, China, , Nini Gao, Qing Wang
In profile monitoring, process monitoring and diagnosis remains and important
and challenging problem. Although the analysis of nonlinear profile data have
been extensively studied in the literature, the challenges associated with diagnosis
of nonlinear profiles with within-profile correlation are yet to be well addressed.
In consideration of within-profile correlation, a Gaussian process model is applied
to model the nonlinear profiles. A practical diagnosis scheme based on Schwarz’s
Bayesian information criterion is proposed to identify the outliers in Phase I.
Simulation results show that the proposed method could effectively find outlying
profiles in a historical dataset.
4 - Adaptive Variability Monitoring Procedure For High-dimensional
Processes
Jinho Kim, Qatar University,
Monitoring process variability of a multivariate process is crucial to ensure high
quality of product. However, monitoring process variability in high-dimensional
processes is considerably difficult due to the large number of variables and the
limited number of samples. In this talk, we present a procedure based on an
adaptive LASSO-thresholding for monitoring changes in the covariance matrix.
The performance of the proposed chart, is evaluated for various shift patterns and
compared with one of the existing penalized likelihood based methods. The
results show the effectiveness of the proposed chart.
WD70
Acoustic- Omni
Transportation, Ops IV
Contributed Session
Chair: Emre Kirac, University of Arkansas, 4207 Bell Engineering
Center, Fayetteville, AR, 72701, United States,
1 - Public Transit Regulation And Subsidization Under
Asymmetric Information
Yanshuo Sun, PhD Candidate, the University of Maryland, College
Park, MD, 20740, United States, Qianwen Guo,
Zhongfei Li
This paper studies how to subsidize a monopolistic public transit operator with
unknown production cost parameters. An incentive-compatibility regulatory
mechanism which induces the operator to report its true parameter are design:
government seeks to maximize social welfare by determining the transit service
parameters and the subsidy to operator to induce its participation, and the
operator implements the operation schedule to meet financial constraint.
Comparison between complete and asymmetric information in terms of fixed cost
and marginal cost are proposed. It will provide an effective tool for designing
policies and evaluating practices regarding public transit subsidization.
2 - Effects Of Multiple Capacity Changes on Congestion Pricing
Model To Handle “Day Of Operations” Airport Capacity Reduction
Abdul Qadar Kara, Asst. Professor, King Fahd University of
Petroleum and Minerals, KFUPM Box 5067, Dhahran, 31261,
Saudi Arabia,
In an earlier work, a model was built on basic econometric principle of congestion
pricing embedded within an optimization model. The model provided a
mechanism to manage airport runway capacity reduction on “day of operations”.
The current work reports further analysis of the model, mainly its response
towards the effect of multiple changes in runway access to arriving flights on both
the schedule and the pricing.
3 - Price-compatible Matching Mechanisms For Carrier Collaboration
Su Xiu Xu, The University of Hong Kong, LG108, Composite
Building, HKU, Pokfulam Road, Hong Kong, 999077, China,
This study is the first extending the existing market design theory to the field of
supply chain and logistics management. It is known that money flow is not
allowed in the matching markets like stable marriage, house allocation, and
kidney exchange. In this study, we explore the potential of lane exchange among
a number of self-interested truckload carriers in a collaboration network. We
propose the (price-compatible) top trading cycles and deals (TTCD) mechanism
and the price-compatible top trading cycles and chains (PC-TTCC) mechanism.
Both mechanisms are effective in terms of the compatibility with money flow,
strategy-proofness, the realized welfare of carriers, and budget balance.
4 - Dynamic Team Orienteering Problem
Emre Kirac, University of Arkansas, 4207 Bell Engineering Center,
Fayetteville, AR, 72701, United States,
Ashlea Bennett Milburn
This study introduces the dynamic team orienteering problem (DTOP), which is a
combinatorial optimization problem with many practical applications such as
humanitarian relief logistics and tourist trip organizations. In DTOP, some
locations are known at planning time while others are dynamic and each
associated with a profit. The goal is to maximize collected profits by visiting a set
of static and dynamic locations throughout a planning horizon within a specified
timeframe. The multiple plan approach (MPA) is adapted to solve DTOP.
Competitive ratio analysis using an offline algorithm is performed to assess the
performance of MPA.
WD71
Electric- Omni
Game Theory IV
Contributed Session
Chair: Manxi Wu, Massachusetts Institute of Technology, 235 Albany
Street, 3112B, Cambridge, MA, 2139, United States,
1 - On Learning How Players Learn: A Mechanical Turk Experiment
Walid Krichene, University of California, Berkeley, Berkeley, CA,
94720, United States,
We consider a noncooperative game in which players compete for resources. In
the online model, the game is played repeatedly and players update their
strategies using an online learning algorithm. We study whether learning
dynamics are descriptive of human behavior. We developed a web application to
simulate the game, and used the Mechanical Turk platform to collect data on
decision dynamics of human players. Using this data, we pose and solve a
dynamics estimation problem, and show that a parameterized online model
(based on the mirror descent method) can be descriptive of players’ decision
dynamics. We give qualitative insights, evaluate the predictive ability of this
model, and discuss its limits.
WD71