2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Agent-based Modeling For Resilience Of Disaster Recovery
Fei He, Texas A&M University, Kingsville, Kingsville, TX,
United States, KumareshBabu Murugesan
Disaster recovery involves multiple stakeholders and various uncertainties
associated with environment and community behaviors. This research use agent-
based modeling and game theory to investigate the uncertainty and
interdependence in the household and business for disaster recovery. The effects
of disaster mitigation, and stake holders’ learning capability to disaster recovery
are investigated.
3 - Modeling Fire Risk And Resource Allocation For Fire Protection
And Safety
Vineet Madasseri Payyappalli, PhD Student, University at Buffalo,
SUNY, Buffalo, NY, United States, ,
Adam Behrendt, Jun Zhuang
Fire-related hazards are an everyday phenomenon, and firefighting in the United
States owe to more than one million firefighters in about 30,000 fire departments
across the country. The estimated total cost of fire was $329 billion in 2011, and
yet there is little work in the literature about risk assessment, cost-benefit
analysis, and resource allocation in fire protection. Using a data-driven study, we
propose empirical and theoretical models to assess risk levels and develop risk-
reduction strategies that include optimal resource allocation, optimal facility
design, and optimal routing solutions.
4 - Crisis Information Distribution Among Official Users In Twitter
Based On Hurricane Sandy
Bairong Wang, University at Buffalo, The State University of
New York, 338 Bell Hall, Buffalo, NY, 14260, United States,
Jun Zhuang
Our study analyzes how crisis information about Hurricane Sandy is distributed
from official Twitter accounts to the common Twitter users based on 6 social
media key performance indicators (KPIs). Our results show that (a) the six KPIs
are significantly different among governmental organizations (GOs), non-
governmental organizations (NGOs) and news agent users; (b) the networks
formed by mention and re-tweet are effective methods to reach more public
members; (c) the information coverage could be expanded to more stakeholders
in disaster scenario; (d) distributing speed is faster among networks formed by
news agent users than GO and NGO users.
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208B-MCC
Environmental Decision Analysis
Sponsored: Decision Analysis
Sponsored Session
Chair: Melissa A Kenney, University of Maryland, College Park, MD,
United States,
1 - Robust Decision Making Methods For Water Resource
Management Under Climate Change Uncertainty
Seth Guikema, University of Michigan,
Julie Shortridge, Ben D Zaitchik
Water resource systems must be managed under deep, long-run uncertainty
about the impacts of climate change on water quantity and quality in a given
basin. Standard approaches for decision making under uncertainty have
limitations in this context. We summarize work done to further develop an
alternative approach, based on the Robust Decision Making method (RDM). RDM
seeks not to find the optimal solution to a given management problem but to find
solutions that are robust in the sense of doing well under a wide range of future
conditions. We demonstrate the method with an application to water resource
management in the Lake Tana basin.
2 - Multi-criteria, Interactive Optimization For Design Of
Watershed Plans
Andrew Hoblitzell, Indiana University Purdue University
Indianapolis, Indianapolis, IN, 46202, United States,
, Meghna Babbar-Sebens,
Snehasis Mukhopadhyay
Multi-objective optimization has yielded numerous algorithms for design of
solutions to real-world planning problems. The inclusion of decision makers
(DMs) within the optimization algorithm’s search process, especially for planning
problems with DM-specific, subjective, qualitative, and/or unquantifiable criteria,
has gained interest recently. Our work focuses on modifications made to our
existing watershed planning decision support system, called WRESTORE.
Interactive genetic algorithms and reinforcement-based machine learning
algorithms are used for search and optimization, while neural networks and other
methods are utilized for the modeling of human users’ criteria.
3 - Using Visualization Science To Diagnose And Improve Global
Change Indicator Understandability
Michael D Gerst, University of Maryland,
Melissa A Kenney
Indicators are variables that stakeholders believe summarize relevant trends. They
have become an increasingly important part of continuous assessment of global
environmental change. For indicators to be effective, they need to be understood
by diverse audiences. Using visualization science, we have diagnosed and
redesigned a set of global change indicators, showing how simple visual changes
can lead to large improvements in understandability.
4 - Understanding Homeowner Decisions On System Configuration
For Parcel Level Storm Water Management
Royce Francis, George Washington University,
, Domenico C Amodeo
A common problem faced by many older U.S. cities is the management of storm
water to prevent the over flow of sewage and pollutants into local waterways.
Many cities have addressed this problem through grey infrastructure
improvements, as well as promoting low impact development (LID) on private
and public property. Previous researchers have proposed models for assessing
optimal LID installations and predicting consumer behavior in response to
incentives. We explore what an optimal storm water management program
would look like in light of these models, with the high level aim of learning best
practices in system configuration from observing consumer choices.
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209A-MCC
Agent-based Modeling and Simulation - Overview
Sponsored: Simulation
Sponsored Session
Chair: Charles M Macal, Argonne National Laboratory, Argonne, IL,
United States,
1 - Agent-based Modeling And Simulation – Overview
Charles M Macal, Argonne National Laboratory,
Agent-based modeling and simulation (ABMS) is an approach to modeling
systems comprised of autonomous, interacting agents. Applications are growing
rapidly in fields ranging from modeling the stock market to predicting the spread
of epidemics. Complex adaptive systems, emergent behavior, and self-
organization are a few of the notions from ABMS. This session provides an
overview of ABMS, covers its foundations, development toolkits and methods,
practical aspects, and the relationship of ABMS to conventional OR. Key ABMS
resources, publications, and communities are identified. It concludes by
suggesting research challenges to advance the field of ABMS for the coming years.
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209B-MCC
Pricing, Revenue Management and Operations
in Retailing
Sponsored: Revenue Management & Pricing
Sponsored Session
Chair: Mehmet Sekip Altug, Assistant Professor, George Washington
University, 2201 G. Street, NW, Washington, DC, 20052, United States,
1 - Assortment Optimization Under A Synergistic Version Of The
Multinomial Logit Model
Venus Lo, Cornell University, Ithaca, NY, United States,
Huseyin Topaloglu
We consider the revenue management problem of offering an optimal subset of
goods when there are product synergies. The traditional multinomial logit choice
model suffers from Independence of Irrelevant Alternatives and offering a larger
subset must decrease each goods’ choice probabilities. Our synergistic model has a
similar structure but offering selected pairs of goods together can boost their
choice probabilities. In the special case where synergy exists in a linear fashion,
we provide an efficient dynamic program and show that the optimal subset can be
found in one step by solving a simple linear program.
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