2016 INFORMS Annual Meeting Program

TC57

INFORMS Nashville – 2016

TC57 Music Row 5- Omni

2 - Applying The Modern Portfolio Theory For A Dynamic Energy Portfolio Allocation In The Electricity Markets Reinaldo Crispiniano Garcia, Associate Professor, University of Brasilia - UnB, Faculty of Technology, Industrial Engineering Department, Brasilia, 70904-970, Brazil, rcgar@yahoo.com, Javier Contreras, Janiele Custodio, Virginia Gonzalez New energy markets undergoing deregulation induce participants to face increasing competition and volatility, where the objective of a Generation Company (Genco) is to maximize their profit while minimizing their risk. This work proposes two MPT models applying the Mean Variance Criteria (MVC) and the Conditional Value at Risk (CVaR) one. The MPT models are combined with a Generalized Autoregressive Conditional Heteroskedastic (GARCH) prediction technique for a Genco to optimally diversify its energy portfolio. The two models are applied to the PJM electricity market showing their capabilities and comparisons between them helping decisions makers to apply these two models as tools for a Genco. 3 - Carbon Dioxide Source Selection And Analysis Xin Li, Pennsylvania State University, University Park, PA, 16803, United States, xzl118@psu.edu, Jose Antonio Ventura, Luis F. Ayala H., Uday Shanbhag This paper aims at identifying and analyzing potential sources of CO2 from power plants and industrial facilities in any geographical location in the U.S. that can be used to supply CO2 for fracturing operation in well pads. Different approaches to capture CO2 from power plants and industrial sources, as well as their corresponding technological maturity, are discussed. Detailed models to calculate costs incurred by CO2 capture in two types of prime candidates for CO2 capture, coal-fired power plants (CPP) and high-purity CO2 sources (HPS), are presented. 4 - Cooptimization Of Series Facts Device Set Points And Generation Dispatch Mostafa Sahraei-Ardakani, Assistant Professor, University of Utah, 1249 E Spence Avenue, Apt 242, Salt Lake City, UT, 85281, United States, mostafa.ardakani@utah.edu No energy or market management system today optimizes the set point of flexible AC transmission system (FACTS) devices along with generation dispatch, due to the computational complexity of the problem. We propose an extremely effective and fast linear programming heuristic that facilitates such co-optimization. As a result, the operation of FACTS devices can be significantly enhanced leading to substantial economic and reliability gains. 5 - A Probabilistic Unit Commitment Model Kenneth Bruninx, Post-doctoral researcher, KU Leuven, Leuven, Belgium, kenneth.bruninx@kuleuven.be, Erik Delarue Stochastic unit commitment models allow calculating an optimal trade-off between the cost of scheduling and activating reserves, load shedding and curtailment, but may become computationally intractable for real-life power systems. Therefore, we develop a probabilistic unit commitment (PUC) formulation, which allows internalizing the reserve sizing and allocation in a deterministic unit commitment problem, considering the full cost of reserve allocation and activation. This PUC formulation yields UC schedules that are nearly as cost-effective as the theoretical optimal solution of the stochastic model in calculation times similar to that of a deterministic equivalent. TC59 Cumberland 1- Omni Robust and Reliable Optimization in Transportation and Logistics Invited: Transportation Science & Logistics Invited Session Chair: Ehsan Jafari, University of Texas, Hart Lane, Austin, TX, 78731, United States, ejafari@utexas.edu 1 - Multicriteria Shortest Path Problem For Electric Vehicles In Stochastic Networks Ehsan Jafari, University of Texas, Hart Lane, Austin, TX, 78731, United States, ejafari@utexas.edu, Stephen Boyles This presentation focuses on the problem of finding a prior path for a single electric vehicle in a network with stochastic travel times. There are a number of non-identical charging stations (different charging prices and charging rates) through the network and charge depletion rate is modeled as a function of arc length and arc travel time. We formulate the problem as a multicriteria shortest path problem with three components: reliability, cost and time.

New Topics in Behavioral Operations Sponsored: Behavioral Operations Management Sponsored Session

Chair: Leon Matias Valdes, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, United States, lvaldes@mit.edu 1 - Observational Learning Through Inventory Availability Information: Empirical And Field Evidence Ruomeng Cui, Kelley School of Business, Indiana University, Bloomington, IN, 47401, United States, cuir@indiana.edu, Achal Bassamboo, Dennis Zhang Consumers, when making such purchasing decisions, tend to be influenced by others’ actions, i.e., observational learning, or the out-of-stock pressure, i.e., product availability information. Using a unique dataset of more than thousands of daily deals, we empirically measure the herding effect. Well-sold products in the last hour tend to attract more customers in the next hour. The phenomenon persists after controlling for alternative explanations such as consumer reviews, search/experience goods and discount depth. We study the underlying drivers: observational learning or out-of-stock risk. 2 - The Decision To Recall: A Behavioral Investigation In The Medical Device Industry The decision to recall can impact a manager’s career and the performance of the firm. We identify a set of situational and dispositional factors that may influence the recall decision despite not being specified by the FDA. We test these factors through an experiment with a Fortune 500 firm. We find that a physician’s inability to detect a defect in the product and understanding the root cause of the defect increases the likelihood of recalling the product but these factors vary across individuals. We find that an individual’s cognitive reflection level helps divide managers into two groups, those who are more influenced by situational factors and those who are more influenced by dispositional factors. 3 - The Behavioral Impact Of Queueing Visibility On Server Effort Allocation. Yaroslav Rosokha, Purdue University, yaroslav.rosokha@gmail.com, Masha Shunko, Julie Niederhoff Using behavioral lab experiments we explore the impact of feedback on workers’ effort allocation in a queueing environment with multiple human servers. We focus on the visibility of workload and the visibility of other servers’ effort as mechanisms controlling feedback. 4 - Supply Chain Visibility And Social Responsibility: Investigating Consumers’ Behaviors And Motives Leon Valdes, Massachusetts Institute of Technology, lvaldes@mit.edu, Tim Kraft, Yanchong Zheng We conduct an experiment to investigate: (i) when does supply chain visibility impact consumers’ valuations of social responsibility (SR)? And (ii) what roles do reciprocal motives and prosocial orientations play in affecting their valuations? We show that consumers value visibility when workers are disadvantaged or when consumers use the lack of visibility as an excuse not to pay for SR. We also observe that high prosocial consumers do not exhibit reciprocal motives, while these motives can have a significant impact on low prosocial consumers’ valuations. Our work thus identifies when there is a revenue benefit to greater visibility and what information best resonates with different consumers. TC58 Music Row 6- Omni Energy XII Contributed Session Chair: Kenneth Bruninx, Post-doctoral researcher, KU Leuven, Leuven, Belgium, kenneth.bruninx@kuleuven.be 1 - The Relationship Between Energy Consumption And Economic Development Yuan Qian, Tsinghua University, Beijing, China, qiany14@mails.tsinghua.edu.cn, Pingke Li This paper estimates the causal relationship between aggregate energy consumption, disaggregate energy consumption and real GDP of China for the 1994-2013 period. Results indicated that bidirectional Granger causality runs from total energy consumption to real GDP and from industrial energy consumption to real GDP but no Granger causality between real GDP and transport energy consumption. George Ball, Indiana University, Kelley School of Business, gpball@indiana.edu, Karen L Donohue, Rachna Shah

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