2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Optimizing Adaptive Stormwater Management With Green
Infrastructure: A Case Study In Wingohocking
Watershed, Philadelphia
Fengwei Hung, Johns Hopkins University, 3400 North Charles
Street Ames Hall 313, Baltimore, MD, 21218, United States,
, Benjamin F Hobbs, Arthur E McGarity
Due to heterogeneous hydrology and uncertain maintenance effectiveness, the
long run performance of Green Infrastructure (GI) for managing urban
stormwater and pollution is highly uncertain. Implementing GI adaptively
provides opportunities to modify plans in response to learning. We apply three
stochastic optimization models for adaptive GI planning that represent monitoring
and active experimentation. The models recommend optimal immediate GI and
learning actions.
3 - Flood Risk Management Using Artificial Avulsions In The Yellow
River Delta
Liang Chen, Johns Hopkins University,
Benjamin Hobbs
Due to high in-channel sedimentation rates, the Yellow River Delta of China has
changed course frequently in its history, with huge socioeconomic impacts. Water
storage and deliberately engineered avulsions can reduce these impacts, but at a
cost. Multi-objective analysis and Monte Carlo simulation is used to develop
decision rules and choose sizes and locations for engineered avulsions and
floodways, considering uncertain future floods and trade-offs between flood risk
and management cost.
4 - The Process Of Co-producing a ClimateIndicators System
Melissa A Kenney, University of Maryland,
In this talk I will discuss the development and implementation of a climate
indicators system that was designed to be owned collaboratively by multiple
Federal agencies and designed to support for undefined climate adaptation and
mitigation decisions. The process of development involved over 200 producers
and users of climate information from the Federal government, academic, and
private sector/NGOs over the past 5 years. I will reflect on the implications and
lessons learned for future co-production processes that similarly adopt best
practices in the development of indicators.
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209A-MCC
Learning for Simulation and Simulation Optimization
Sponsored: Simulation
Sponsored Session
Chair: Giulia Pedrielli, National University of Singapore, TBD,
Singapore, TBD, Singapore,
1 - Simulation Analytics For Virtual Statistics
Yujing Lin, Northwestern University,
“Virtual statistics” are performance measures that are conditional on the
occurrence of an event; the virtual waiting time of a customer arriving to a queue
at time t is one example. We describe methods for estimating virtual statistics
post-simulation from the retained sample paths, examining both their small-
sample properties and asymptotic consistency.
2 - The Effects Of Estimation Of Heteroscedasticity On
Stochastic Kriging
Xi Chen, Virginia Polytechnic Institute and State University,
In this talk, we discuss the effects of using smoothed variance estimates in place of
the sample variances on the performance of stochastic kriging (SK). Different
variance estimation methods are investigated, and we show that such a
replacement leads to improved predictive performance of SK. An SK-based dual
metamodeling approach is further proposed to obtain more accurate prediction
results given a fixed simulation budget.
3 - Extended Kernel Regression Method To Combine Analytical
Methods And Simulation
Andrea Matta, Shanghai Jiao Tong University,
Simulation is widely adopted to predict system performance. The main drawback
is that it is slow in execution and the related computer experiments can be very
expensive. On the other hand, analytical methods can rapidly provide system
estimates but they are approximate. Recently the Extended Kernel Regression
(EKR) has been proposed to combine simulation with analytical methods. This
work has different purposes: 1) test EKR on different cases; 2) compare EKR with
other state of the art techniques; 3) propose two different methods for calculation
of confidence bands. Numerical results show the EKR method provides accurate
predictions, particularly when DOE size is small.
4 - G-STAR A New Kriging Based Trust Region Method For
Global Optimization
Giulia Pedrielli, Arizona State University,
Szu Hui Ng
Stochastic Trust region methods iteratively generate meta-models for local
optimization. We propose the Global Stochastic Trust Region Augmented Method
(G-STAR): local meta-models are iteratively improved, through a new sampling
criterion balancing exploration and exploitation. Specifically, a global model
guides the search, while local models are fitted using sampled points in the
generated trust regions. The best point is predicted at each iteration through an
ensemble of the global and the local meta-models generated along the search.
Preliminary numerical tests show an improved performance with respect to the
previously proposed extended Two Stage Sequential Optimization algorithm.
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209B-MCC
Revenue Management with Strategic Customers
Sponsored: Revenue Management & Pricing
Sponsored Session
Chair: Woonghee Tim Huh, University of British Columbia, Vancouver,
BC, Canada,
Co-Chair: Jaelynn Oh, University of Utah, Salt Lake City, UT, United
States,
1 - Dynamic Pricing In The Presence Of Strategic Consumers: Theory
And Experiment
Anton Ovchinnikov, Queen’s School of Business, 143 Union
Street, Kingston, ON, K7L 3N6, Canada,
Mirko Kremer, Benny Mantin
We investigate the behavior of retailers who sell a fixed inventory of products
over a two period horizon (main selling season followed by a markdown period)
to a mixture of myopic and strategic consumers. We present a stylized model and
an experimental study. Our main result is that retailers myopically underprice
when facing consumers who are strategic. We explore the drivers for such
underpricing and show that it is related to a counter-intuitive model prediction
that most revenue is obtained at markdowns.
2 - Choosing To Be Strategic: Implications Of The Endogenous
Adoption Of Forward-looking Purchasing Behavior On
Multiperiod Pricing
Arian Aflaki, Duke University, 923 White Pine Drive, Durham, NC,
27705, United States, , Pnina Feldman,
Robert Swinney
We consider whether strategic consumer behavior benefits consumers when they
purchase from a revenue-maximizing firm that sets prices over multiple periods.
We show that many consumers have lower surplus if they are strategic than if
they are myopic. We then develop a model in which consumers choose to become
strategic by exerting costly effort, and show that considering this choice can have
a significant qualitative impact on firm and consumer decisions. In addition, we
illustrate that it is possible to increase firm profit and consumer welfare
simultaneously by increasing the cost of strategic behavior. Finally, we find that
price commitment can encourage more strategic waiting and harm firms.
3 - Product Quality And Pricing Management
Ruxian Wang, Johns Hopkins Carey Business School, Baltimore,
MD, 21202, United States, , Shiliang Cui
Product quality, price and service are arguably the most important factors
consumers consider in purchasing a product. We investigate a firm’s strategy for
managing multiple products under various monopolistic and oligopolistic settings.
Our analytical results show that the optimal quality level of any product should
always be set at the global optimum, even if the firm can change price
simultaneously, faces other firms’ competition, or offers a free ancillary service.
Moreover, consumer surplus may be higher.
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