2015 Informs Annual Meeting

MB64

INFORMS Philadelphia – 2015

MB64 64-Room 113A, CC Joint Session DAS/ENRE: Environmental Decision Analysis: Theory and Applications Sponsor: Decision Analysis Sponsored Session Chair: Melissa Kenney, Research Assistant Professor, University of Maryland, 5825 University Research Court, Suite 4001, College Park, MD, 20740, United States of America, kenney@umd.edu 1 - Decision Analysis for Sustainable Management of the Yellow River Delta Liang Chen, Student, Johns Hopkins University, 3900 N Charles St. Apt. 1302, Baltimore, MD, 21218, United States of America, chenliang1468@gmail.com, Benjamin Hobbs, Jeff Nittrouer, Hongbo Ma, Andrew Moodie We develop a stochastic programming model for channel management and flood control that can characterize risks and impacts of possible natural avulsions, and provides solutions for prevention and mitigation. Reflecting the physical mechanism in coastal delta, our model imbeds a 1D hydrodynamic model to simulate sediment transport, channel aggradation and flooding. Our case study is Huanghe (Yellow River) Delta, China, one of the world’s most dynamic and heavily urbanized coastal landscapes. 2 - Adaptive Stormwater Management with Green Infrastructure using Two-stage Stochastic Programming Fengwei Hung, Student, The Johns Hopkins University, 3400 N. Charles Street, Ames Hall 313, Baltimore, MD, 21218, United States of America, fwhung0807@gmail.com, Benjamin Hobbs, Arthur Mcgarity Green Infrastructure manages stormwater with natural processes involving significant uncertainty. Thus, many cities choose to implement it adaptively to learn how it works. We define “learning” as updating of distribution parameters of the stochastic program’s coefficients, representing: automatic learning, triggering learning, multi-state learning, and multi-stage learning with technology improvement. Finally, we calculate risk-return tradeoffs for a Philadelphia stormwater case study. 3 - Framing Effects Created by Ambiguity Aversion in Static Decisions Erin Baker, University of Massachusetts, MIE Department, 220 ELAB, Amherst, MA, United States of America, edbaker@ecs.umass.edu, Eva Regnier In climate policy-making, many respected economists recommend using ambiguity-averse decision rules. The vulnerabilities created by ambiguity aversion in dynamic decision making have been demonstrated previously. We show that even in static, one-time, decisions, ambiguity-averse decision rules make policy makers susceptible to bias created by framing effects. 4 - Using Multi-criteria Decision Analysis to Explore Management Options in the Grand Canyon Michael C. Runge, USGS Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, MD, 20708, United States of America, mrunge@usgs.gov, Kendra Russell, Kirk E. Lagory The Bureau of Reclamation and the National Park Service are developing a Long- term Experimental and Management Plan (LTEMP) for managing water releases from the Glen Canyon Dam and related activities. We conducted multi-criteria decision analysis to evaluate the proposed alternatives, integrating scientific input from a dozen modeling teams, and values-focused input from a wide set of deeply-involved stakeholder groups. We used value-of-information analysis to inform experimental design.

3 - A Multistage Stochastic Programming Model for the Optimal Surveillance & Treatment of Invasive Species Eyyub Kibis, Graduate Research Assistant, Wichita State University, 1845 N Fairmount, Wichita, KS, 67260, United States of America, eyyubyunus@gmail.com, Esra Buyuktahtakin, Robert Haight In this study, we develop a multistage stochastic programming model to address the invasive species surveillance and treatment while minimizing the expected damages of invasive species. We use a discontinuous discrete decision tree and incorporate discretized surveillance decisions along with the probabilities of each scenario into the spatially-explicit model. The model allows policy makers to take the best surveillance and treatment decisions over time by exploiting various scenarios. 4 - Import Inspections: Harnessing Enforcement Leverage to Prevent Invasive Species Introductions Rebecca Epanchin-Niell, Resources for the Future, Washington DC, United States of America, epanchin-niell@rff.org, Michael Springborn, Amanda Lindsay Allocating scarce border inspection resources over a diverse set of imports to prevent invasive pest entry presents a substantial policy design challenge. We develop a risk-based inspection system in which sampling intensities vary across imports based on risk. We determine optimal sampling of imports to minimize invasive pest introduction accounting for strategic responses of exporters. Daniel H. Wagner Prize Competition II Cluster: Daniel H. Wagner Prize Competition Invited Session Chair: Allen Butler, President & CEO, Daniel H. Wagner Associates, Inc. 2 Eaton Street, Hampton, VA 23669, United States of America, Allen.Butler@va.wagner.com 1 - Integrated Planning of Multi-type Locomotive Service Facilities under Location, Routing and Inventory Considerations Kamalesh Somani, CSX Transportation, 500 Water St, Jacksonville, FL, 32202, United States of America, Kamalesh_Somani@CSX.com, Xi Chen, Yanfeng Ouyang, Siyang Xie, Zhaodong Wang, Jing Huang Long term infrastructure planning of locomotive service facilities is vital to the efficiency of the railroad. We developed a large-scale optimization model that integrates decisions on (i) location, capability, and capacity of fixed facilities, (ii) home location and routing plan of movable facilities, and (iii) assignment of a variety of service demands. A decomposition-based solution framework was developed and shown to bring significant economic benefits in full-scale implementations. 2 - Scheduling Crash Tests at Ford Motor Company Daniel Reich, Leadership Program, Ford Motor Company, Dearborn, MI, United States of America, dreich8@ford.com, Amy Cohn, Ellen Barnes, Yuhui Shi, Marina Epelman, Erica Klampfl We present the problem of scheduling crash tests for new vehicle programs at Ford. We developed a completely custom-made scheduling system that transforms a labor-intensive scheduling process relying on high levels of expertise, to a more automated one that utilizes optimization and institutionalizes expert knowledge. Our system enables engineers and managers to consider multiple scheduling scenarios, using efficient interfaces to specify problem instances and efficient methods to solve them MB63 63-Room 112B, CC

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