2015 Informs Annual Meeting

MB60

INFORMS Philadelphia – 2015

MB60 60-Room 111A, CC Cases in the Undergraduate OR Curriculum Sponsor: INFORM-ED Sponsored Session Chair: Michael Veatch, Gordon College, 255 Grapevine Rd, Dept. of Mathematics, Wenham, MA, 01984, United States of America, Mike.Veatch@gordon.edu 1 - Strategies for using Cases in the Undergraduate Classroom Matthew Drake, Associate Professor Of Supply Chain Management, Duquesne University, 925 Rockwell Hall, 600 Forbes Avenue, Pittsburgh, PA, 15282, United States of America, drake987@duq.edu Students often develop a better understanding of quantitative material by applying the analytical techniques to realistic decision scenarios. Cases provide OR/MS instructors with an effective vehicle to introduce applications of business analytics in practice. While they are common at the graduate level, cases are not used as often with undergraduates. In this session we will discuss strategies for using cases effectively with undergraduate students. 2 - On the Development of Case Studies for an Undergraduate Business Analytics Course Eric Huggins, Professor Of Management, Fort Lewis College, 1000 Rim Drive, Durango, CO, 81301, United States of America, huggins_e@fortlewis.edu Over the past three years I have developed half a dozen case studies for an undergraduate business analytics course. Each case study started as a big data set with a few objectives attached to it, and with the help of my students, they have evolved into current (for now), relevant (I think), interesting (I hope) OR-related case studies. 3 - Where Do I Find Classroom Cases? James Cochran, Professor Of Applied Statistics And The Rogers- spivey Faculty Fellow, University of Alabama, P.O. Box 870226, Tuscaloosa, AL, 35487-0226, United States of America, jcochran@cba.ua.edu Many OR/analytics instructors want to incorporate short cases into their undergraduate courses but have difficulty finding suitable, relevant, and topical cases. Where can an instructor find such cases? If s/he is willing to experiment with writing cases, s/he can find the bases of cases in the news, popular culture, and her/his own life (and perhaps publish their efforts in INFORMS Transactions Today). We will demonstrate through several examples from the speaker’s experience writing cases. 4 - Teaching Undergraduate Analytics using Cases MB61 61-Room 111B, CC Stochastic and Robust Optimization Models in Electric Power Systems Sponsor: ENRE – Energy I – Electricity Sponsored Session Chair: Andy Sun, Assistant Professor, Georgia Institute of Technology, 755 Ferst Drive, Atlanta, GA, 30332, United States of America, andy.sun@isye.gatech.edu 1 - Two-stage Distributionally Robust Unit Commitment with Generalized Linear Decision Rules Yuanyuan Guo, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109, United States of America, yuanyg@umich.edu, Ruiwei Jiang, Jianhui Wang It is challenging to accurately estimate the joint probability distribution of the renewable energy. In this paper, based on a small amount of marginal historical data, we propose a two-stage distributionally robust unit commitment model that considers a set of plausible probability distributions. This model is less conservative than classical robust unit commitment models and more computationally tractable by using generalized linear decision rules. Peter Bell, Ivey Business School at Western University, 1255 Western Road, London, ON, N6G 0N1, Canada, pbell@ivey.uwo.ca, Mehmet Begen, Fredrik Odegaard Ivey’s undergraduate analytics courses have used cases extensively for many years. This interactive presentation will discuss some of the benefits (and costs) of a case-based approach to undergraduate teaching.

2 - Stochastic Unit Commitment with Topology Control Recourse for Renewables Integration

Jiaying Shi, University of California, Berkeley, CA, United States of America, United States of America, shijy07@Berkeley.edu, Shmuel Oren

We introduce a two stage stochastic unit commitment formulation in which the second stage recourse actions include possible reconfiguration of the transmission grid through line switching. Switching action in the second stage are determined by a heuristic method. Such topology control capability can mitigate adverse variability in realized renewables output and improve unit commitment efficiency. 3 - Multistage Robust Unit Commitment with Dynamic Uncertainty Sets Alvaro Lorca, Georgia Tech, 251 10th St. NW Apt. A622, Atlanta, GA, 30318, United States of America, alvarolorca@gatech.edu We present a multistage robust unit commitment model with renewables and storage using a simple but effective affine policy for dispatch decisions, while considering dynamic uncertainty sets that integrate wind and solar power resources taking into account spatial and temporal correlations. Our solution algorithm contains enhancements that allow solving the resulting problem efficiently. We also present simulation experiments to evaluate the benefits of our approach. 4 - Multi-stage Stochastic Unit Commitment with SDDP Jikai Zou, Graduate Research Assistant, Georgia Institute of Technology, 755 Ferst Dr. NW, Atlanta, GA, 30332, United States of America, jikai.zou@gatech.edu, Shabbir Ahmed, Andy Sun Despite the great amount of research, stochastic unit commitment (UC) problems, where binary commitment decisions adapt to uncertainty with a multi-stage structure, still remain one of the most challenging stochastic programming problems. In this paper, we investigate a sampling based algorithm that combines stochastic dual dynamic programming (SDDP) and the integer L-shaped method for solving multistage stochastic UC. Numerical results and algorithmic improvement will be discussed. MB62 62-Room 112A, CC Optimization Approaches for Invasive Species and Pest Management Sponsor: ENRE – Environment I – Environment and Sustainability Sponsored Session Chair: Esra Buyuktahtakin, Assistant Professor, Wichita State University, 1845 N Fairmount, Wichita, KS, 67260, Wichita, United States of America, Esra.Buyuktahtakin@wichita.edu 1 - Optimal Inspection of Imports to Prevent Invasive Pest Introduction Robert Haight, USDA Forest Service, Northern Research Station, St. Paul, MN, United States of America, rhaight@fs.fed.us, Rebecca Epanchin-niell, Cuicui Chen Based on our work with USDA-APHIS, we study an acceptance sampling problem that incorporates several features of quality control in public safety programs, including the simultaneous inspection of many heterogeneous lots, a budget constraint that limits inspection, inspection error, and an objective of minimizing cost to consumers. We apply our results to inspecting live plant imports to prevent invasive pest introduction. 2 - Cost-effective Planning of Invasive Species Surveillance with the Maximum Expected Coverage Concept Denys Yemshanov, Research Scientist, Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste Marie, ON, P6A2E5, Canada, Denys.Yemshanov@NRCan-RNCan.gc.ca, Robert Haight, Frank Koch, Bo Lu, Jean Turgeon, Ronald Fournier We present two invasion survey models based on the maximum expected coverage principle (MECP). The models maximize the expected number of invaded sources that are covered by the surveys, where a source is covered if at least one of its transmission pathways connects to a surveyed destination. We present one- and two-stage models designed to survey invasive forest pests in Canada and the U.S. Overall, the approach provides flexible solution to survey the long-distance spread of invasive pests.

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