Informs Annual Meeting 2017

T E C H N I C A L S E S S I O N S

Sunday, 8:00 - 9:30AM

How to Navigate the Technical Sessions

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There are four primary resources to help you understand and navigate the Technical Sessions: • This Technical Session listing, which provides the most detailed information. The listing is presented chronologically by day/time, showing each session and the papers/abstracts/authors within each session.

310B Decision Analytic Approaches to Green

Infrastructure Solutions Sponsored: Decision Analysis Sponsored Session Chair: Fengwei Hung, Johns Hopkins University, Baltimore, MD, 21218, United States, hfengwe1@jhu.edu Co-Chair: Melissa A. Kenney, University of Maryland, College Park, Snehasis Mukhopadhyay, IUPUI, Indianapolis, IN, United States, smukhopa@iupui.edu, Andrew Hoblitzell, Meghna Babbar-Sebens WRESTORE is a stakeholder-driven design system that optimizes best management practices for the Eagle Creek Watershed in Indiana. The system provides resources on conservation programs and enables stakeholders to visualize the impacts of alternative practices on the watershed. The system utilizes fuzzy logic, neural networks, and deep learning for user modeling, and uses an interactive genetic algorithm with mixed initiative interaction for exploratory search and exploitative optimization. In this talk, we will provide a discussion of the current implementation of the system, as well as a brief discussion of ongoing work and future plans for the system. 2 - Understanding Residential Low Impact Development at the Home Owner Level Domenico Amodeo, George Washington University, Washington, DC, 20052, United States, dcamodeo@email.gwu.edu Environmental engineers and economist have studied the patterns of adoption for green technology. Many of these studies rely on regression and aggregated data. We developed a zero-truncated negative binomial regression model using parcel level data. However, we argue that regression is an inadequate method for predicting home owner adoption of LID. A graph based method is presented to identify configurations of LID emerging from residential property owners who are presented with a municipal incentive to adopt. We argue that understanding the configurations that emerge may provide insight into homeowners motivations and the effectiveness of incentive programs. 3 - A Two-stage Stochastic Programming Model for Adaptive Stormwater Management with Green Infrastructure Fengwei Hung, Johns Hopkins University, 3400 N Charles St. Ames Hall 313, Baltimore, MD, 21218, United States, hfengwe1@jhu.edu Adaptive management (AM) can be viewed as a multi-stage planning problem with the emphasis on learning by doing and uncertainty. The proposed model considers the decision maker’s risk attitudes and the potential learning in latter stages. Risk is modeled in the form of conditional value of risk (CVaR) and learning is defined as the process of updating random distributions which are assumed to depend on the investment now (1st stage decisions). This method can explore the risk-expected value tradeoffs accounting for what we may be able to learn from doing. Finally, we show an example to demonstrate how this model works. 4 - Optimal Planning of Green Infrastructure Placement in an Urban Watershed under Precipitation Uncertainty Masoud Barah, University of Tennessee, Knoxville, TN, United States, mbarah@utk.edu, Anahita Khojandi, Jon Hathaway, Xueping Li Urbanization, infrastructure degradation, and climate change have overwhelmed most stormwater management systems across the nation or rendered them ineffective. Green Infrastructures (GIs) are low-cost strategies that can contribute to stormwater management. We develop a stochastic programming model to determine the optimal placement of GIs across a set of candidate locations in a watershed to minimize the excess runoff in medium-term precipitation uncertainties. We calibrate the model using rainfall projections and stormwater system’s hydrologic responses to them. We provide optimal GI placement in a watershed and perform sensitivity and robustness analyses to provide insights. MD, 20740, United States, kenney@umd.edu 1 - WRESTORE Design System: Optimizes Best Management Practices

The Session Codes

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Room number. Room locations are also indicated in the listing for each session.

Time Block. Matches the time blocks shown in the Program Schedule.

The day of the week

Time Blocks

Sunday - Monday 8:00am - 9:30am 10:00am - 10:50am 11:00am – 12:30pm 1:30pm – 3:00pm 3:10pm - 4:00pm 4:30pm – 6:00pm Tuesday 7:30am - 9:00am 9:40am - 10:30am 10:30am - 12:00pm 12:05pm - 1:35pm 2:00pm - 3:30pm 3:40pm - 4:30pm 4:35pm – 6:05pm Wednesday 7:30am - 9:00am 9:40am - 10:30am 10:30am - 12:00pm 12:30pm - 2:00pm

Rooms and Locations /Tracks All tracks / technical sessions will be held in the George R. Brown Convention Center. Roo¡m numbers are shown on the Quick Reference and in the Technical session listings. Monday and Tuesday Plenary talks will be held in Hilton- Ballroom of Americas, Level 2

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