2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
505
2 - Correlation Based Adaptive Sampling Strategy For Online
Monitoring Of Correlated High Dimensional Data Streams
Mohammad Nabhan, Georgia Institute of Technology,
Jianjun Shi
Effective process control of High dimensional data with embedded spatial
structure has been an arising challenge, due to the inability of classical techniques
to detect changes in such processes. This article proposes an adaptive sampling
technique that achieves better change detection power by identifying and
exploiting the hidden spatial structure. The adaptive nature of the proposed
method allows for effective monitoring with partial observations. Simulation
studies are performed to validate the efficacy of the proposed monitoring scheme.
This is followed by real data case studies to evaluate the performance under
practical scenarios.
3 - An Effective Online Data Monitoring And Saving Strategy For
Large-scale Climate Simulations
Xiaochen Xian, University of Wisconsin - Madison, Madison, WI,
Kaibo Liu
Large-scale climate simulation models have been developed and widely used to
generate historical data and study future climate. This long-duration simulation
process creates huge amount of data; however, how to effectively monitor and
record the climate changes still remains to be resolved. To address this issue, we
propose an effective online data monitoring and saving strategy over the temporal
and spatial domains with the consideration of practical storage and memory
capacity constraints. Specifically, our proposed method is able to intelligently
select and record the most informative extreme values in the raw data in the
context of better monitoring climate changes.
4 - A Wavelet-based Penalized Mixed-effects Model For Multichannel
Profile Detection Of In-line Raman Spectroscopy
Xiaowei Yue, Georgia Institute of Technology, ,
Hao Yan, Jin Gyu Park, Richard Liang, Jianjun Shi
Modeling of high-dimension nonlinear profiles is an important and challenging
topic in statistical process control. Conventional mixed effect model has
limitations in solving the multichannel profile detection in nanomanufacturing. A
wavelet-based penalized mixed-effects model (WPMM) is proposed to exploits a
regularized high-dimensional regression with linear constraints to decompose
profiles into four parts: fixed effect, normal random effect, defective random
effect, and noise. An accelerated proximal gradient algorithm is developed to
efficiently estimate parameters. Case study shows that the WPMM can realize a
better detection power and shorter computation time.
WE69
Old Hickory- Omni
Dynamic Programming /Control
Contributed Session
Chair: Jefferson Huang, Postdoctoral Associate, Cornell University,
Ithaca, NY, United States,
1 - Segmentation Of Anatomical Structures Using Dynamic
Identification And Classification Of Edges (dice) Model In
Medical Images
Maduka M. Balasooriya, Teaching Assistant, Southern Illinois
University, Edwardsville, Edwardsville, IL, 62026, United States,
Sinan Onal, Xin W Chen
Segmentation of anatomical structures using medical images such as MRI, CT
scan, and digital fundus image is still ongoing research subject. We developed the
dynamic identification and classification of edges (DICE) model that aims to
automatically identify edges of a contour of an anatomical structure without any
intervention from domain experts. The DICE model includes three sequential but
intertwined steps: (a) identifying potential edge points of a contour using moving
range control charts; (b) extrapolating additional edge points of a contour through
noise reduction; and (c) classifying points into different edges using neighborhood
gradient search.
2 - Military Applications Of Approximate Dynamic Programming:
Optimizing Helicopter Lift Operations
James Grymes, Instructor, United States Military Academy,
646 Swift Rd, West Point, NY, 10996, United States,
Military commanders rely on helicopter lift assets to serve as force multipliers by
moving military personnel and equipment around the battlefield. Inefficiency is a
byproduct of uncertainty inherent in military operations which can lead to
mission delays and possible failures. This research is designed to explore
approximate dynamic programming as a tool for assisting air movement planners.
We model Army helicopter lift operations as a Markov Decision Process and learn
the value of decisions through machine learning. The algorithm returns an
approximating value function for scheduling air lift routes in order to enhance
combat power.
3 - Value Function Discovery In Markov Decision Processes
Sandjai Bhulai, Vrije Universiteit Amsterdam, Faculty of Sciences,
De Boelelaan 1081a, Amsterdam, 1081 HV, Netherlands,
, Martijn Onderwater, Robert van der Mei
We introduce a novel method for discovery of value functions for Markov
Decision Processes (MDPs). This method is based on ideas from the evolutionary
algorithm field. Its key feature is that it discovers descriptions of value functions
that are algebraic in nature. This feature is unique, because the descriptions
include the model parameters of the MDP. The algebraic expression can be used
in several scenarios, e.g., conversion to a policy, control of systems with time-
varying parameters. We illustrate its application on an example MDP.
4 - Making College Admission Offers: A Dynamic
Programming Approach
Subhamoy Ganguly, Indian Institute of Management Udaipur,
IIM Udaipur, MLSU Campus, Udaipur, 313001, India,
Michele Samorani, Ranojoy Basu,
Viswanathan Nagarajan
College admissions problem refers to the problem where each college seeks to
admit the best possible class from a pool of applicants and most applicants apply
to multiple colleges, hoping to enroll in one of their most preferred colleges. Most
of the extant literature models this problem in the context of a two-sided
matching market. However, admissions offices often cannot use these approaches
due to practical limitations. We develop a dynamic programming model that
could help colleges make optimal decisions of offering admission to secure the
best possible class while filling all seats.
5 - Reductions Of Undiscounted Markov Decision Processes And
Stochastic Games To Discounted Ones
Jefferson Huang, Postdoctoral Associate, Cornell University, Ithaca,
NY, United States, Eugene A Feinberg
We provide conditions under which certain total and average cost Markov
decision processes (MDPs), with possibly uncountable state and action spaces, can
be reduced to discounted ones. These reductions are used to obtain complexity
estimates for computing an optimal policy for finite MDPs and for computing a
nearly optimal policy for infinite MDPs. We also provide analogous reductions for
zero-sum stochastic games with possibly uncountable state and action spaces, and
show how they can be used to obtain results on the existence of the value and
optimal strategies, as well as results on robust MDPs.
WE71
Electric- Omni
Game Theory V
Contributed Session
Chair: Edward Cook, Senior Vice President, Capital One, 4 Bisley Court,
Henrico, VA, 23238, United States,
1 - Bayesian Opponent Exploitation In Imperfect-information Games
Sam Ganzfried, Ganzfried Research, 55 West 26th Street #36E,
New York, NY, 10010, United States,
For all game classes one can potentially do better than following a static Nash
equilibrium strategy by learning to exploit perceived weaknesses of opponents.
An exact efficient algorithm is known for best-responding to the opponent’s
posterior distribution assuming a Dirichlet prior with multinomial sampling in
normal-form games; however, for imperfect-information games the best known
algorithm is a sampling algorithm approximating an infinite integral without
theoretical guarantees. The main result is the first exact algorithm for
accomplishing this in certain imperfect-information games. We also present an
algorithm for the natural setting where the prior is uniform over a polyhedron.
2 - Strategic Decentralization: Implications For Equity And Equality
Omkar D Palsule-Desai, Faculty, Indian Institute of Management
Indore, Rau Pithampur Road, Wing C, Ground Floor, Indore,
453331, India,
We develop a noncooperative game theoretic model to examine network
performance and stability related implications of allocation mechanisms that
endogenously balance equity vis-à-vis equality, and hence, the degree of collusion
among the network firms in a decentralized setting. We show that inefficiencies
and instability of decentralization can be eliminated by incorporating an
additional degree of freedom in the network formation game. Our model and the
structural results are applicable to networks such as producers’ cooperatives,
industrial clusters, joint production and research facilities, etc., wherein the
conflicts of equity-equality and degree of collusion are predominant.
WE71