2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
179
Demand-driven Movie Scheduling In A Multiplex
Julia Charlotte Krake, Research Associate, University of Hamburg -
Institute for Operations Research, Von-Melle-Park 5, Hamburg,
20146, Germany,
A cinema chain is confronted with the weekly problem of creating a movie
schedule. Decisions need to be taken about the set of movies, the assignment to
screens and the start times. The proposed model improves this decision-making
process with possible objectives like the increase of total attendance or revenue.
Unittracker: Enhancing Conventional Generation Modeling
Resolution In The Regional Energy Deployment System
(ReEDS) Model
Venkat Krishnan, Engineer, National Renewable Energy
Laboratory, 15013 Denver West Parkway, Golden, CO, 80401,
United States, , Jonathan Ho,
Kelly Eurek
The Regional Energy Deployment System (ReEDS) model is the National
Renewable Energy Laboratory’s flagship planning model for projecting the long-
term build-out and operation of the U.S. electric power generation and
transmission system. In this poster, we explore the effects of model resolution on
solution quality and tractability. Specifically, for each of 134 load balancing areas
in ReEDS, we increase the resolution of conventional plant thermal efficiencies
and examine the consequent impacts on the planning results from ReEDS.
Network Simplex Based Algorithm For The Minimum Cost Flow
Problem With Linear Interdependencies
Adam Rumpf, Illinois Institute of Technology,
10 West 32nd Street, Room 208, Chicago, IL, 60616, United States,
We consider a generalization of the minimum cost network flow problem in
which the flows through certain arcs are bounded by linear functions of the flows
through other arcs. This formulation can be used to model interdependent
infrastructure systems, for example a subway system whose components require
delivery of electrical power from a separate system. We characterize the basis of
this problem as a spanning forest plus some supplementary structures, and use
these to develop an efficient solution algorithm based on the well-known
network simplex algorithm. This is joint work with H. Kaul.
Modelling Competitive Equilibrium Prices For Energy
And Balancing Capacity In Electricity Markets Involving
Non-convexities
Andre Ortner, Researcher, Technical University of Vienna,
Gusshausstraße 25-29, Vienna, 1040, Austria,
, Daniel Huppmann
In economic analyses of markets often the dual variables of market clearing
equations derived from the optimal solution of cost-benefit optimization models
are interpreted as efficient market prices. Whereas in convex (linear) problems
the validity of this approach is undisputed it cannot be generalized to problem
formulations containing non-convexities. The withholding of spinning reserves in
electricity markets are a good example of such cases as costs in these markets are
essentially driven by indivisibilities. In this paper we present a novel modelling
approach designed to find equilibria in binary games to derive equilibrium prices
of self-committed electricity market models.
Multi-period Matching Under Relaxed Stability
Zihao Li, Georgia Institute of Technology, 765 Ferst Drive,
School of ISyE, Atlanta, GA, 30332-0225, United States,
Özlem Ergun, Julie L Swann
Organizations sometimes face challenges in producing assignments of staff to jobs
that are good (in the sense that preferences are met), and stable (which promotes
high morale and less turnover in staff). In some situations, assignments have to be
made more than once, allowing organizations to be more flexible in making
assignments and negotiate with workers more efficiently. We extend the stable
matching problem to a multi-period setting and consider stability as measured
over all periods. We consider notions of relaxed stability to improve the quality of
matching by analyzing stability over the entire horizon of the assignment.
Stochastic Optimal Power Flow With Forecast Errors And
Failures In Communication
Basel Alnajjab, Lehigh University, 19 Memorial Drive,
West, Bethlehem, PA, 18015, United States,
Alberto J Lamadrid, Rick S Blum, Shalinee Kishore,
Lawrence V Snyder
The role of communication networks in supporting the operation of power grids
will become increasingly more critical as we continue to integrate renewable
energy sources into power grids, We present a stochastic optimal power flow
formulation in which we account for errors in forecasting future load and
renewable generation while also considering random failures in the
communication network employed to communicate the realized values of the
quantities for which we have forecasts. The communication network is also
assumed to be employed for the control of loads and generators in the power
system. We present results comparing different topologies for the communication
network.
Reducing Response Categories In Multinomial Regression
Brad Price, West Virginia University, College of Business and
Economics, PO Box 6025, Morgantown, WV, 26506, United States,
Adam Rothman, Charles Geyer
In this work we propose penalized likelihood estimators to reduce the number of
response categories in multinomial regression. Typically the multinomial model is
made simpler by trying to reduce the number of covariates in the model. We
instead approach it by combining response categories in situations where a set of
covariates does a poor job of differentiating between these categories. An ADMM
algorithm is proposed, and convergence properties presented. Tuning parameter
selection is also addressed.
Reliability And Economic Criteria To Determine Management
Policies Of Wind Energy Systems With Storage
Cristina M Azcarate, Universidad Publica de Navarra, Dep of
Statistics & OR, Campus Arrosadia, Pamplona, Navarra, 31006,
Spain, Fermin Mallor, Pedro Mateo
Electrical energy storage systems integrated into a renewable generation system
reduce the effects of forecasting errors and enable the determination of
management policies. In this work, we propose a management strategy for a wind
energy system with storage capacity that integrates tactical and operational
decisions in a single stochastic mathematical model. The mathematical model
includes economical and reliability criteria, and an updated probabilistic wind
speed forecast. A simulation model inspired on a real wind-hydrogen energy
system is built to assess the performance of this strategy.
Electric Transmission Expansion Considering Property Value
Reduction On Routing
Juan Andrade, University of Texas at Austin, 4405 Avenue A, Apt
11, Austin, TX, 78751, United States,
Ross Baldick
The development of utility scale renewable generation requires new transmission
infrastructure, whose proximity to urban areas produces social opposition. This
opposition can be quantified as a social cost produced in property value reduction
by transmission proximity to population. It is presented a MILP formulation that
minimizes costs for generation, and investment and social impact for transmission
considering an electrical and a routing networks. An implementation that uses
geographical information was developed, and tested with typical IEEE test
systems.
Detection Of Copyrightable Images From Social Media Feeds
Manoj Pooleery, Scopio LLC, 175 Varick St, New York, NY,
United States, Binu Josephi, Jinjin Qin
Finding ``original” images-those that can be potentially protected by copyright
laws-from social media channels is a challenging problem. The images may
contain objectionable/unusable content (spam) and are often modified by users
by addition of text, change of texture and color. This paper presents a framework
for detection of original images by first filtering out spam, then identifying
presence of user generated text and finally augmenting the decision making
process by manual curation & verification. Empirical results obtained from Twitter
& Instagram feeds suggest that an automated technique for identification of
original images with minimal manual intervention can be developed.
Application Of Data-driven Analytics To Optimal Decisions
Meng-Chen Hsieh, Assistant Professor I, Rider University, 2083
Lawrenceville Rd, SWG 313, Lawrence Township, NJ, 08648,
United States, , Jeffrey Simonoff,
Clifford Hurvich, Avi H Giloni
In operation research and management science problems, a traditional approach
in deriving optimal decision rules under uncertainty has been to optimize an
univariate target quantity while ignoring the presence of auxiliary variables.
These auxiliary variables, if used wisely, can provide valuable information on
their association with the target variable and thus substantially reduce the target
variable’s prediction uncertainty. This work provides guidelines on applying
statistical learning to leverage the association between target and auxiliary
variables thereby enhancing the efficiency of optimal decisions.
Modeling In-Process Machining Data as Spatial Point Clouds vs.
Time Series: Research Challenges and Opportunities
Mohammed Shafae, PhD Candidate, Virginia Tech, Blacksburg, VA,
United States, Lee Wells, Jaime Camelio
Traditional approaches for analyzing machining process data revolve around
representing them as time-series. What tends to be missing is the relationship
between the time-series and the part physical dimensions. This research discusses
the concept of a novel representation of machining data as spatial point clouds
and the corresponding practical advantages.
POSTER SESSION