Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

WC44

3 - Time Space Combinatorial Optimization Problems and Applications Hadi Farhangi, Visiting Assistant Professor, Savannah State University, 204, 3219 College Street, 3219 College Street, Savannah, GA, 31404, United States This work presents a time extension of some the combinatorial optimization problems. Particularly, the variables are redefined to include time into the problem. The resulting models are disjoint combinatorial problems that can be connected using coupling constraints. The properties of these problems and appropriate solution methods are presented in this work. Finally, some applications of the time space combinatorial problems are discussed. n WC42 North Bldg 227A Practice- Simulation I Contributed Session Chair: Matthew J. Saltzman, Clemson University, Dept of Mathematical Sciences, Martin Hall Box 340975, Clemson, SC, 29634-0975, United States 1 - GPU Supported Large Scale Simulation Models for Influenza Pandemic Outbreaks Shalome Hanisha Anand Tatapudi, University of South Florida, 4202 E. Fowler Avenue, ENB 118, Tampa, FL, 33620-5350, United States, Zhila Nouri, Tapas K. Das Influenza pandemics are a serious concern and researchers are trying to understand its patterns. One such tool to effectively understand the disease characteristics is through an agent-based (AB) simulation model, which is versatile, yet has computational limitations when it comes to simulating larger populations. This study integrates the flexibility of AB simulation with computational efficiency of a graphical processing unit (GPU) to create models for pandemic outbreaks in large areas comprising of hundreds of millions of people. 2 - Comparison of MRSA Infection Control Policies in ED Patients Karthick Srinivasan, Rochester Institute of Technology, Rochester, NY, 14623, United States, Vignesh Krishnan Rajkumar, Levi Toweh, Nasibeh Azadeh Fard Methicillin-Resistant Staphylococcus Aureus (MRSA) is a major cause of preventable nosocomial infections in ED. The changes made in admission policies of patients with MRSA can affect patient throughput in hospitals. In this research, we study the impact of admission policy change for MRSA patients in an ED of a large hospital in upstate NY using discrete-event simulation modeling. 3 - An Agent-based Model of Subsidized Flooding Insurance Valerie Washington, University of Michigan, Ann Arbor, MI, 48109, United States Flood insurance is one strategy for addressing the economic impact of floods to both homeowners and their community. In this paper, I use agent-based modelling to explore how income-based subsidies influence mitigation strategies employed by agents, and whether that includes large-scale abandonment. I investigate the effect of subsidized and unsubsidized flood insurance on community mitigation decisions, damages incurred, and vacancy and move out rates. Damages are evaluated from the perspective of individual homeowners and the community at large. 4 - Review and Analysis of Airplane Boarding Strategies Based on Discrete Events Simulation Alejandro Garcia del Valle, Professor, University of A. Coruna, Escuela Politecnica Superior, C/ Mendizabal S/N - Esteiro, A. Coruna, 15403, Spain, Roi Sanchez-Tutor, Diego Crespo-Pereira, Javier Faulin Airport taxes are one of the most critical economic factors for an airline due to the time the plane stays on the airport while turn-around. Boarding is a key part of turn-around for both customer satisfaction and airline profitability. This is the reason why so many strategies have been designed in order to reduce boarding times. By using Discrete Event Simulation, different boarding strategies are analyzed to determine which one is more efficient in Boeing 737-800 according to different scenarios considering plane occupation, delays and 2-door boarding. 5 - What Not to Expect When You’re Expecting: Perils of Sampling and Estimating for Lognormal Distributions Matthew J. Saltzman, Associate Professor, Clemson University, Dept of Mathematical Sciences, Martin Hall Box 340975, Clemson, SC, 29634-0975, United States, William C. Bridges, Neil J. Calkin Lognormal distributions can be problematic when the variance of the underlying normal distribution is other than very small. We illustrate these problems in terms of sampling issues, interval estimation of the mean, and comparison of lognormal and logbinomial distributions with similar means and variances.

n WC43 North Bldg 227B NREL Session Emerging Topic: Energy and Climate Emerging Topic Session Chair: Brady Stoll, National Renewable Energy Laboratory, CO, United States 1 - Modeling Challenges of High Photovoltaic Penetrations in Future Electric Grids Brady Stoll, NREL, 15013 Denver West Parkway, Golden, CO, 80401, United States, Elaine Thompson Hale, Jennie Jorgenson As states and utilities incentivize low carbon electricity and the cost of solar photovoltaics (PV) drop, it is likely PV penetrations will continue to increase. We utilized a capacity expansion model, the Resource Planning Model, to study the impacts of high solar penetrations in the western United States through 2035. We here describe the creation of high solar penetration scenarios and operational improvements needed to accurately model these scenarios, including updated operating reserve requirements and curtailment requirements. Additionally, we present our findings on the increased operability of the system in 2035 when these modeling improvements were included. 2 - Late-century Electric Sector Climate Impacts Using a High-resolution Capacity Expansion Model Stuart Cohen, National Renewable Energy Laboratory, 15013 Denver West Parkway, RSF 300, Golden, CO, 80401, United States Climate impacts on the electric sector will vary in space and time while also being influenced by market and policy evolution. I’ll discuss how the National Renewable Energy Laboratory’s ReEDS model of U.S. electric sector expansion is being used to examine climate impacts on load, system performance, and water resources while accounting for regional, diurnal, and seasonal differences; technology-specific responses; and market-policy developments. ReEDS has recently been modified to solve through the year 2100, enabling this presentation to explore climate impacts in the mid-to-late century within the context of highly uncertain human and natural system development. 3 - Deep Reinforcement Learning for Urban Energy Management and Demand Response Jose Vazquez Canteli, University of Texas at Austin, Austin, TX, United States The increasing amount of sensor data from buildings can help improve the energy management in urban settings. Reinforcement learning (RL) is a self-tuning and model-free controller that learns from real-time and historical data. RL is scalable and attractive for demand response in residential buildings. We developed CityLearn, a simulation environment based on CitySim, an urban energy simulator, and TensorFlow, a library that allows implementations of machine learning algorithms. We applied CityLearn in a case study with multiple buildings, controlled by RL, that compete against each other to reduce their cost of electricity, which increases when they consume electricity simultaneously. n WC44 North Bldg 227C Models and Optimization Methods for Future Electricity Markets Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Mahnoosh Alizadeh 1 - Competitive Market with Renewable Power Producers Achieves Asymptotic Social Efficiency Yue Zhao, Stony Brook University, 127 Light Engineering, Stony Brook, NY, 11794, United States A price-making two-settlement power market in which both conventional generators and renewable power producers (RPPs) participate is studied. It is proved that the Nash Equilibrium (NE) of the market converges to the social optimum as the number of RPPs increases. As a result, social efficiency is asymptotically achieved with a simple market mechanism for integrating RPPs, without the need for an independent system operator to perform a centralized stochastic optimization. The analytical derivation of the NE offers an elegant characterization of the market power of the competitive RPPs. The market outcomes predicted by the developed theoretical results are demonstrated by simulation studies.

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