2016 INFORMS Annual Meeting Program

MB58

INFORMS Nashville – 2016

2 - Behavioral Drivers Of Routing Decisions: Evidence From Restaurant Table Assignment Bradley R Staats, University of North Carolina at Chapel Hill, Campus Box 3490, McColl 4721, Chapel Hill, NC, 27599-3490, United States, bstaats@unc.edu, Fangyun Tan In many settings, humans make routing decisions dynamically, either because algorithms don’t exist, decision support tools have not been implemented, or existing rules are not enforced. Understanding how individuals make decisions creates the opportunity to identify both positive deviances, as well as suboptimal decision making that can be improved. In this paper we theoretically identify the factors that may impact decision making before empirically examining a large operational data set in a casual restaurant setting to research whether and how hosts deviate from their predefined round-robin rule to seat customers to servers. 3 - The Impact Of Delay Announcements: An Experimental Approach Gad Allon, Northwestern University, Evanston, IN, United States, g-allon@kellogg.northwestern.edu, Achal Bassamboo, Mirko Kremer We explore the impact of delay announcements by studying the data from a lab experiment, where customers are provided with anticipated delay. 4 - Diagnostic Accuracy In Congested Environment Mirko Kremer, Frankfurt School of Finance and Management gGmbh, m.kremer@fs.de, Francis E DeVericourt The trade-off between diagnostic accuracy and congestion characterizes many manufacturing and service settings, where the gathering of additional information is likely to improve the diagnosis but may also increase congestion in the system. For example, medical staff often needs to weigh the benefit of running additional tests against the cost of delaying the provision of services to other patients. We present the results from a set of controlled laboratory experiments designed to test the predictions of a formal sequential testing model that captures this trade- off. MB58 Music Row 6- Omni Energy VI Contributed Session Chair: Luis Baringo, Universidad de Castilla-La Mancha, Av. Camilo José Cela s/n, E.T.S.I.Industriales, Ciudad Real, 13071, Spain, Luis.Baringo@uclm.es 1 - Resilient Based Power System Restoration On Sectionalized Grid Saeedeh Abbasi, Research and Teaching Assistant, University of Houston, 9000 Braesmont Dr, Apt #4, Houston, TX, 77096, United States, sabbasi5@uh.edu, Masoud Barati, Gino J Lim Several catastrophic experiences of extreme events increased the criticality of the power grid restoration. This paper discusses a novel resilience-based restoration and sectionalizing model. This restoration approach aims to restore the de- energized power grids to the normal state after cascading outages that may occur during severe conditions. The problem is formulated as a bi-level programming model and solved by the pre-emptive programming method. The proposed approach is illustrated using an IEEE six-bus and 118 bus test systems, with focus on assessing and improving its resilience during the restoration process to severe disasters. 2 - A Generation Capacity Expansion Planning Model Considering Capacity Markets With High Wind Power Penetrations Jonghwan Kwon, Arizona State University, 10410 N Cave Creek Rd, Tempe, AZ, 85020, United States, Jonghwan.Kwon@asu.edu, Zhi Zhou, Todd Levin, Fernando de Sisternes, Kory W Hedman, Audun Botterud This work aims to develop a modeling framework for simulating generation capacity expansion planning, considering long-term capacity markets and short- term energy and operating reserve markets with increasing levels of wind power. The framework will provide the ability to analyze the impact of high wind penetrations on the economics and reliability of the grid in a more realistic market environment. System operators and regulators can obtain new and important insights into how wind resources can be efficiently and effectively integrated into electricity markets under various rules and policies.

3 - Electricity Pooling Markets With Inelastic Demand Mohammad Rasouli, PhD Candidate, University of Michigan, 430 S Fourth Ave, Ann Arbor, MI, 48104, United States, rasouli@umich.edu, Demosthenis Teneketzis In the restructured electricity industry, electricity pooling markets are an oligopoly with strategic producers possessing private information. We focus on pooling markets where aggregate demand is represented by a non-strategic agent and is inelastic. Inelasticity of demand is a main difficulty in electricity markets. It can potentially result in market failure and high prices. We propose a market mechanism that has the following features. (F1)It is individually rational.(F2)It is budget balanced.(F3)It is price efficient(F4)The energy production profile corresponding to every non-zero Nash equilibrium of the game induced by the mechanism is a solution maximizes the social welfare. 4 - Offering Strategy Of A Virtual Power Plant: A Stochastic Adaptive Robust Optimization Approach Luis Baringo, Universidad de Castilla-La Mancha, Av. Camilo José Cela s/n, E.T.S.I. Industriales, Ciudad Real, 13071, Spain, Luis.Baringo@uclm.es, Ana Baringo We propose a stochastic adaptive robust optimization model for the offering strategy of a virtual power plant (VPP) that participates in the day-ahead and the real-time energy markets. The VPP comprises a conventional power plant, a wind-power unit, a storage facility, and flexible demands, which participate in the markets as a single entity in order to optimize their energy resources. Uncertainties in the wind-power production and in the market prices are modeled using confidence bounds and scenarios, respectively. Connected and Automated Vehicles Sponsored: Transportation Science & Logistics Sponsored Session Chair: Michael Levin, University of Texas, Austin, TX, United States, michaellevin@utexas.edu 1 - Modeling Spatiotemporal Propagation Of Information In a Connected Vehicle System With The Consideration Of Communication Capacity Jian Wang, Purdue University, Lyles School of Civil Engineering, West Lafayette, IN, United States, wang2084@purdue.edu Xiaozheng He, Yong Hoon kim, Srinivas Peeta This study proposes integro-differential equations to model the spatiotemporal propagation of information under vehicle-to-vehicle communications while factoring communication capacity and traffic dynamics. We also derive closed form solutions for the asymptotic speeds of information propagation wave under different densities of equipped vehicles. Numerical experiments demonstrate the effectiveness of the proposed model in various traffic conditions. 2 - Road In Transition: Autonomous Vehicle Manufacturer Strategies And Transportation Systems Performance Mohamadhossein Noruzoliaee, University of Illinois at Chicago, Chicago, IL, United States, h.noruzoliaee@gmail.com, Bo Zou, Yang Liu This study explores the impacts of autonomous vehicles (AVs) on transportation system equilibrium and AV manufacturer pricing strategies. A mathematical program with equilibrium constraints (MPEC) is formulated, where the upper level determines pricing strategy of an AV manufacturer and the lower-level computes system equilibrium as a variational inequality (VI). Besides, the competition among multiple AV manufacturers is formulated as an equilibrium problem with equilibrium constraints (EPEC). Solving the MPEC and EPEC helps gauge the impact of market-driven AV pricing on system performance. 3 - A Cell Transmission Model For Dynamic Lane Reversal With AutonomousVehicles Michael Levin, University of Texas, Austin, TX, United States, michaellevin@utexas.edu Autonomous vehicles admit consideration of novel traffic behaviors such as reservation-based intersection controls and dynamic lane reversal. We present a cell transmission model formulation for dynamic lane reversal. For deterministic demand, we formulate the dynamic lane reversal control problem for a single link as an integer program and derive theoretical results. In reality, demand is not known perfectly at arbitrary times in the future. To address stochastic demand, we present a Markov decision process formulation. Due to the large state size, the Markov decision process is intractable. However, based on theoretical results from the integer program, we derive an effective heuristic. We demonstrate significant improvements over a fixed lane configuration both on a single bottleneck link with varying demands, and on the downtown Austin network. MB59 Cumberland 1- Omni

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