Informs Annual Meeting 2017

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INFORMS Houston – 2017

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formance. All these numerical analysis results show that the proposed method could be of great help for decision makers not only for restoration stage but also for identifying critical road section sets that need more attention for mainte- nance.

361E Energy Contributed Session Chair: Site Wang, Clemson University, Clemson, SC, United States, sitew@clemson.edu 1 - Modeling Frequency Regulation in Isolated Power Systems Miguel Carrion, Universidad de Castilla-La Mancha, Avda Carlos II I.s/n, Toledo, Spain, miguel.carrion@uclm.es, Rafael Zarate- Miñano, Miguel Cañas, Ruth Domínguez Isolated power systems are particularly vulnerable to power plant failures. In order to guarantee the supply security in these systems, electrical energy is provided by a large number of small-sized generating units. Because of the low inertia of these units, frequency fluctuations in isolated systems are much higher than in interconnected systems. Considering this framework, we propose a stochastic unit commitment with frequency regulation constraints to model the operation of a renewable-dominated isolated power system. Uncertain renewable units and demand side management actions are explicitly considered. The proposed formulation is tested in a realistic case study. 2 - Arbitrage Pricing in Revenue Cap Regulation of Electricity Distribution Timo Kuosmanen, Aalto University, Runeberginkatu 22-24, Helsinki, Finland, timo.kuosmanen@aalto.fi, Tuan Nguyen Revenue cap is a widely used instrument in the regulation of local monopolies such as electricity and gas distribution firms. Due to its simplicity, most regulators apply Capital Asset Pricing Model (CAPM) to set the rate of return for the capital invested. However, the expected return and associated risks dependent on several latent factors besides the single market factor used in CAPM. Arbitrage Pricing Theory (APT) provides a more general and flexible approach to assess the risk and return. This paper explores the practical aspects of applying APT in regulation. We examine the real world example of revenue cap regulation of electricity distribution firms in Finland. 3 - An Energy Infrastructure Adaptation Framework for Changing Climatic Conditions Site Wang, Clemson University, 100 Freeman Hall, Clemson University, Clemson, SC, 29634, United States, sitew@clemson.edu Site Wang, Los Alamos National Labotoary, TA-03, Building 1690, Room 101C, Los Alamos, NM, 87545, United States, sitew@clemson.edu, Russell Bent, Carleton Coffrin, Donatella Pasqualini, Nathan Urban, Sandra D.Eksioglu, Scott J. Mason In recent years, global climate change has highlighted potential issues for coastal area energy infrastructure reliability. Changing climate increases uncertainty in predicting extreme weather such as tidal incursion and stronger, more frequent hurricanes. As it is critical to adapt energy infrastructures to these potential risks, we present a stochastic optimization framework for infrastructure adaptation to uncertain weather events using state-of-the-art climate simulation models for stochastic quantification. Our framework can provide decision makers with forward-thinking plans that consider adaptation costs and risks under exogenous stochastic hurricane events. 361F Design of Transportation Infrastructure and Services Sponsored: TSL, Intelligent Transportation Systems (ITS) Sponsored Session Chair: Yu Zhang, University of South Florida, 4202 E. Fowler Ave., ENB118, Tampa, FL, 33620, United States, yuzhang@usf.edu 1 - A New Resilience Measure for Infrastructure Restoration Plan Optimization Considering Unmet Demand in Transportation System after Disruptive Event Tingting Zhao, Research Scholar, University of South Florida, 4202 E Fowler Avenue, Tampa, FL, 33620, United States, tingtingzhao@mail.usf.edu, Yu Zhang A bi-level optimization problem is formulated for restoration plan optimization of transportation infrastructure system after a disruption, e.g. flood, hurricane or other natural disasters. The upper-level problem is to maximize the system resilience index, combining mobility and accessibility, with limited resources. The lower-level one is a network flow assignment problem considering unmet demand in the system after the disruption. We propose an efficient heuristic algorithm for solving this problem and examine its performance with a typical road network (Sioux-Falls network). With multiple random disruptions in the experiment system, enormous combinations of damaged links are generated. The restoration plan optimization method proposed in this work could effectively identify the links to be restored with resource constrains. Furthermore, we vary the number of damaged links to examine its impact on system resilience per- TB53

2 - Scheduling Models in Multimodal Transportation Networks with Commuter Preferences Julia Y. Yan, Massachusetts Institute of Technology, Cambridge, MA, 02143, United States, jyyan@mit.edu, Dimitris Bertsimas, Yeesian Ng Modern transportation systems in urban settings are increasingly complex. Public transportation services are often operated at large scale and under tight budget constraints. Furthermore, passengers are able to make choices between a variety of commuting options. We develop novel formulations for minimizing system backlog in multi-modal networks, while accounting for operator budget concerns and commuter preferences. Our formulations can solve within minutes, and we show improvement over heuristics through simulations on real data from the Boston MBTA network. 3 - A Stochastic Programming Approach for Electric Vehicle Charging Network Design Sina Faridimehr, Wayne State University, 4815 Fourth Street, Manufacturing Engineering Building, Detroit, MI, 48202, United States, sina.faridimehr@wayne.edu, Saravanan Venkatachalam, Ratna Babu Chinnam Advantages of electric vehicles (EV) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large- scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community. We conducted computational experiments using various publicly available data sources, and benefits of the solutions are evaluated both quantitatively and qualitatively for a given community. 4 - Revenue Management in Urban Parking Systems with Learning and Competition Yuguang Wu, University of Wisconsin, Madison, WI, United States, wu376@wisc.edu, Qiao-Chu He, Xin Wang We consider decentralized garages who maximizes their revenue when the parking demand is uncertain but spatially correlated. A “smart parking” company provides demand forecast to increase system efficiency. At an individual garage level, we analyze the optimal operational strategy to leverage its limited capacity. At a system level, we investigate the optimal parking data sharing policy for the society. We aim to provide guidelines for the “smart parking” company concerning the design of information system towards more efficient utilization of urban parking resources. 5 - Optimal Snow Removal Facility Locations for Stochastic Snowfall Yufeng Zhang, University of Minnesota, 500 Pillsbury Drive S.E., Room 175, Minneapolis, MN, 55455, United States, zhan4879@umn.edu, Avinash Unnikrishnan, Alireza Khani A MIP formulation is proposed for the optimal locations of snow truck stations to minimize the capital and operating cost of snow removal operations. Uncertainties of snowfalls are modeled by a chance constraint, which takes the correlation of snowfalls into consideration. The stochastic problem is reformulated to a MICQP to make it practically applicable for realistic problems. Several variations of the problem are solved based on the real case of the state of Minnesota. 362A R&D Issues in Agribusiness Invited: Agricultural Analytics Invited Session Chair: Saurabh Bansal, Penn State University, Penn State University, State College, PA, 16801, United States, sub32@psu.edu 1 - Optimizing the Classification of Soybean Varities as Elite Varities in a Three Stage Experimentation Durai Sundaramoorthi, Washington University in Saint Louis, 10352 Conway Road, Saint Louis, MO, 63131, United States, dsundaramoorthi@gmail.com, Yujin Lee As world population grows, the, need for more food is increasing. On the other hand, land available for farming is reducing. Agribusinesses use a multi-year testing strategy to narrow down the choice of new varieties introduced by breeders. Syngenta -an agribusiness - uses a three stage experimentation to choose a handful of elite soybean varieties at the end of stage three by starting with thousands of varieties in stage one. We use machine learning and optimization to identify elite varieties at the end of stage three. TB54

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