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SA20 ■ SA20 20- West 211 B- CC Joint Session: SPPSN/ENRE-Environment: Energy Policy and Sustainability Sponsor: Public Programs, Service and Needs & Energy, Natural Res & the Envi/ Environment and Sustainability Sponsored Session Chair: Erin Baker, University of Massachusetts, Amherst, MA, United States of America, edbaker@ecs.umass.edu 1 - Decommissioning and Repowering Decisions for Renewable Power Producers Chenlu Lou, Iowa State University, 3004 Black, Ames, IA, 50011, United States of America, clou@iastate.edu, K. Jo Min There have been substantial increases in the construction and operation of wind farms across the U.S.A. and the world, which will lead to a significant number of wind farm decommissioning and repowering decisions soon. We formulate and analyze mathematical models from a real options perspective with the operation and maintenance cost following the geometric Brownian motion process, and derive managerial insights for relevant government policy makers. 2 - Hydrogen Production Facility Network Design from Stochastic Green Energy Supply Source Jorge Barnett Lawton, Zaragoza Logistics Center, C/ Bari 55, Plaza, Edificio Nayade, Portal 5, Zaragoza, 50197, Spain, jbarnett@zlc.edu.es, Mozart Menezes, Jarrod Goentzel Hydrogen has been identified as a clean alternative to store energy from volatile sources. We analyze the production and distribution network design problem of a firm generating electricity from wind and producing hydrogen by electrolysis, in the presence of stochastic energy prices and supply. We provide solution approaches using column generation, and use our model to evaluate a future hydrogen supply chain in Spain, under different distribution policies and government incentive schemes. 3 - R&D Portfolio Analysis of Low Carbon Energy Technologies for Climate Change Mitigation Rose Zdybel, University of Massachusetts-Amherst, 120D Marston Hall, Amherst, MA, United States of America, rozoe@msn.com We analyze R&D portfolios of low carbon advanced energy technologies for climate change mitigation. We use the GCAM integrated assessment model to analyze the effects of combinations of low carbon energy technologies on CO2 concentration stabilization costs and then combine the results with probabilistic data from expert elicitations. We also develop econometric relationships between variables to estimate results for input values other than those that are part of the elicitations. 4 - A Decision Support System for Managing Wind-turbines with Storage in Smart-grids Frederic Murphy, Professor, Temple University, Alter Hall 435, Philadelphia, PA, 19122, United States of America, fmurphy@temple.edu, Fernando Oliviera We analyze the wind-turbine business model given the opportunities created by the smart grid and by efficient electric battery storage systems. We model the interaction between dynamic prices and wind-regimes; we analyze how batteries can be used to improve wind-turbines reliability and profitability. We study how forward and option contracts can be used by a wind-turbine to hedge risk and we use conditional value at risk to assess the wind-turbine risk exposure and as a basis for risk-hedging. ■ SA21 21- West 212 A- CC Applications of Networks and Graphs Contributed Session Chair: Zeynep Ertem, Texas A&M University, Industrial & Systems Engineering, 3131 TAMU, College Station, TX, 77843, United States of America, zeynep84@tamu.edu 1 - Detecting Research Fronts using Citation Network Analysis Katsuhide Fujita, The University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo, 113-8656, Japan, fujita@ipr-ctr.t.u-tokyo.ac.jp, Yuya Kajikawa, Junichiro Mori, Ichiro Sakata One of the important methodologies for detecting the research fronts is the citation network analysis. Especially, citation network analysis with combinational types or weights has possibilities of finding the new research fronts compared with non-weighted and single type of citation networks. We perform a comparative study to investigate the each type of citation networks in detecting a research front using visibility, speed, topological relevance and keyword similarity. INFORMS Phoenix – 2012 62 2 - Social Network Analysis at Wine and Food Festival Bomi Kang, Associate Professor, Coastal Carolina University, P O Box 261954, Conway, SC, United States of America, bkang@coastal.edu, Young Jae Kim, Taylor Damonte Social network analysis emphasizes the importance of constructing the interconnectedness among concepts and knowledge networks in respondents’ minds. The technique has gained popular use in social science over the past three decades. Using concept maps and degree centralities derived from social network analysis, authors demonstrated new insights in festivals in Myrtle Beach Area, through this novel technique. 3 - Traffic Grooming in Optical Networks: Decomposition and Partial LP Relaxation Hui Wang, PhD Candidate, North Carolina State University, 2152 Burlington Labs, Raleigh, NC, 27695, United States of America, hwang4@ncsu.edu, George Rouskas We consider the traffic grooming problem, a fundamental network design problem in optical networks. We propose a new decomposition approach with two subproblems: (1) virtual topology and traffic routing (VTTR), and (2) routing and wavelength assignment (RWA). We also propose an ascending utilization iterative algorithm based on partial LP relaxation to further improve the scalability of VTTR. Our approach delivers a desirable tradeoff between running time and quality of the final solution. 4 - Graph Theoretical Analysis of Brain Networks Zeynep Ertem, Texas A&M University,Industrial & Systems Engineering, 3131 TAMU, College Station, TX, 77843, United States of America, zeynep84@tamu.edu, Sergiy Butenko Exploring the connectivity patterns of human brain is gaining more interest in the last decade. Correlated low frequency fluctuations in the blood oxygenation level dependent signal have been widely observed in connected brain regions. Resting state functional magnetic resonance imaging (R-fMRI) consists of hundreds of time periods for thousands of image voxels. Our aim is to model the brain as a complex network. The clusters are identified with graph theoretical analysis. ■ SA22 22- West 212 B- CC Topics in Real Time and Embedded Computing Sponsor: Computing Society Sponsored Session Chair: Alex Mills, Indiana University Kelley School of Business, 1309 E. Tenth Street, Bloomington, IN, 47405, United States of America, millsaf@indiana.edu 1 - Optimization-based Frameworks for Dynamic Configuration of Real-time Systems with Energy Constraints Eduardo Camponogara, Professor, Federal University of Santa Catarina, Cx. P. 476, Florianopolis, 88040, Brazil, camponog@das.ufsc.br, Riad Nassiffe, George Lima Embedded real-time systems powered by batteries require suitable support for energy-savings at the operating system level. Mechanisms to do so must take into consideration not only energy constraints but also schedulability since tasks must execute within predefined time windows. Further, it is desired that application quality of service (QoS) is optimized. This talk presents two optimization-based frameworks for maximizing application QoS subject to both schedulability and energy constraints. 2 - Can Randomness Buy Clairvoyance? Stochastic Scheduling of Mixed Criticality Real-time Job Systems Bader Al-Ahmad, PhD Student, University of British Columbia, 2205 Lower Mall, P.O. Box 70, Vancouver, BC, V6T1Z4, Canada, baderalahmad84@gmail.com, Sathish Gopalakrishnan We pose the problem of stochastically scheduling mixed criticality jobs when job execution time distributions at all criticality levels are given. By stochastic, the dynamical evolution of the system criticality level over time is captured by a stochastic process. We ask: is such information any beneficial in reducing the pessimism of current deterministic algorithms? Our objective is to match as closely the performance of a powerful oracle that knows the future system behavior realization. 3 - Temporal Logic Testing for Cyber-physical Systems Georgios Fainekos, Arizona State University, 699 S. Mill Ave., Tempe, AZ, 85281, United States of America, Georgios.Fainekos@asu.edu, Sriram Sankaranarayanan One of the major challenges in Model Based Development (MBD) of Cyber- Physical Systems (CPS) is the verification of real-time functional system properties. In general, the problem is undecidable due to the interplay between continuous and discrete system dynamics. In this talk, we present how the problem of verification can be posed as an optimization problem which can be solved using stochastic optimization techniques including Monte-Carlo methods and the Cross Entropy method.
■ SA23 23- West 212 C- CC Survivable Networks Sponsor: Telecommunications Sponsored Session Chair: Abdulah Konak, Pennsylvania State Berks, Tulpehocken Road, P.O. Box 7009, Reading, PA, 19610, United States of America, auk3@psu.edu 1 - Survivable Capacitated Layered Networks with Spanning Tree Protection Tachun Lin, Cameron University, 2800 W Gore Blvd, Lawton, OK, 73505, United States of America, tlin@cameron.edu, Zhili Zhou, Krishnaiyan Thulasiraman In this paper, we study the survivability problem in capacitated layered networks. We propose necessary and sufficient conditions for the existence of survivable routing with upper-layer protection, formulate this problem as a mixed-integer program, and design a novel upper-layer spanning tree protection algorithm which guarantees the upper-layer network connectivity and generates routings within lower-layer capacity. Computational experiments demonstrate the efficiency of our algorithm. 2 - Resilience: A New Metric to Evaluate Network Reliability/Survivability Ozgur Kabadurmus, Auburn University, 3333 Shelby Center, Auburn, AL, 36849, United States of America, ozk0001@tigermail.auburn.edu, Alice E. Smith In telecommunication network design problems, survivability and reliability are used to evaluate quality of service. In this study, a new metric that combines network reliability with network resilience is presented to measure reliability/survivability more effectively. It is compared with well-known network reliability/survivability metrics, namely k-connectivity and two terminal reliability, and its benefits and computational efficiency are discussed. 3 - Lexicographical Minimization of Routing Hops in Hopconstrained Node Survivable Networks Luis Eduardo Neves Gouveia, CIO and DEIO, Cidade Universiteria, Bloco C6, Campo Grande, Lisbon, 1749-016, Portugal, legouveia@fc.ul.pt, Pedro Patricio, Amaro de Sousa We address the following traffic engineering problem: how to route deterministic changes of original demands in a pre-dimensioned network, maintaining network survivability and QoS, while minimizing the number of hops of either i) all service paths or ii) the worst service path for each commodity, in a lexicographical sense. We present and discuss two classes of Integer Linear Programming models for this problem and computational results considering several traffic demand settings. 4 - A Network Support System for Ad-hoc Networks using Autonomous Intelligence Agents Abdulah Konak, Pennsylvania State Berks, Tulpehocken Road, P.O. Box 7009, Reading, PA, 19610, United States of America, auk3@psu.edu This paper presents a decentralized approach to maintain the connectivity of a MANET using autonomous, intelligent agents. Concepts from the social network analysis along with flocking algorithms are utilized to maintain the connectivity of mobile nodes. Computational results are presented to demonstrate the effect of various local agent behaviors on the global network connectivity. ■ SA24 24- West 213 A- CC Advances in Modeling and Optimal Control of Infectious Disease Spread Sponsor: Health Applications Society Sponsored Session Chair: Reza Yaesoubi, Post-Doctoral Research Fellow, Harvard Medical School, 641 Huntington Ave., Boston, MA, 02115, United States of America, reza.yaesoubi@gmail.com 1 - A Model for Optimal Allocation of HIV Prevention and Treatment Funds Margaret Brandeau, Stanford University, Huang Engineering Center, Stanford, CA, 94305, United States of America, brandeau@stanford.edu, Sabina Alistar, Elisa Long, Eduard Beck We develop a model to determine the optimal allocation of resources between HIV prevention and treatment services to minimize the basic reproduction number. Our model includes multiple populations with different transmission modes, as well as production functions that relate investment in prevention and INFORMS Phoenix – 2012 63 SA25 treatment to changes in transmission and treatment rates. We apply our model to examine HIV epidemic control in two different settings, Uganda and Russia. 2 - Evaluating the Effectiveness of Interventions During an Influenza Pandemic Anna Teytelman, PhD Student, Massachusetts Institute of Technology, Operations Research Center, Cambridge, MA, 02139, United States of America, teytanna@mit.edu, Richard Larson With pandemic influenza, pharmaceutical and non-pharmaceutical resources are limited. We present dynamic, multi-regional, vaccine allocation schemes for the USA. For single communities with heterogeneous populations, we examine the effects of timing and targeting strategies on reaching herd immunity. We conclude with strategies for combining vaccines with non-pharmaceutical interventions such as hand-washing, public awareness campaigns and school closures to decrease intensity of an outbreak. 3 - Resource Allocation for Optimal Control of Epidemics: Can Treatment Compete? Sabina Alistar, Stanford University, P.O. Box 17244, Stanford, CA, 94309, United States of America, ssabina@stanford.edu Controlling infectious diseases such of HIV requires judicious resource allocation between treatment and prevention. We use an optimal control approach to determine how the allocation decision will change over time, as an epidemic evolves. Our model considers several epidemiological objectives relevant in practice, and non-linear epidemic dynamics. We characterize the optimal solution, identify conditions for optimality, and provide numerical examples. 4 - A Framework for Real-time Decision Making During Epidemics Reza Yaesoubi, Post-Doctoral Research Fellow, Harvard Medical School, 641 Huntington Ave., Boston, MA, 02115, United States of America, reza.yaesoubi@gmail.com, Ted Cohen We propose a novel framework for optimizing decisions during infectious disease epidemics based on real-time observations from the epidemic and resource availability. The framework is flexible and can incorporate diseases of varying natural history and a broad range of interventions. Using influenza and tuberculosis as examples, we demonstrate how the framework can be used to inform decisions to optimize population health within constraints imposed by resource availability. ■ SA25 25- West 213 B- CC Optimization Methods in Radiation Therapy Sponsor: Health Applications Society Sponsored Session Chair: Timothy Chan, Assistant Professor, University of Toronto, 5 King’s College Rd, Toronto, Canada, tcychan@mie.utoronto.ca 1 - Imaging Based Treatment Plan Optimization for Glioblastoma Jan Unkelbach, Massachusetts General Hospital, 30 Fruit Street, Boston, MA, United States of America, junkelbach@partners.org, Bjoern Menze, Ender Konukoglu, Helen Shih Treatment planning for radiotherapy consists of two problems: 1) determining external beam parameters to realize a desired dose distribution, and 2) determining the desired dose distribution from clinical and biological data including dose-response relations, imaging data, or histology. Quantitative approaches to integrate these sources of data involve both mathematical modeling approaches a well as statistical machine learning methods. This approach is discussed for treatments of gliomas. 2 - Spatiotemporally Adaptive Radiotherapy for Hypoxic Tumors Fatemeh Saberian, University of Washington, Seattle, WA, United States of America, saberian@uw.edu, Archis Ghate Lack of oxygen (hypoxia) decreases tumor radiosensitivity and hence can reduce treatment efficacy. We present a stochastic control approach to design treatment plans that adapt to spatiotemporal variations in hypoxia. 3 - Beam Orientation Optimization in Radiation Therapy Treatment Planning Troy Long, Student, University of Michigan, 1205 Beal Ave., Room 1791, Ann Arbor, MI, 48109, United States of America, troylong@umich.edu, Edwin Romeijn Beam orientation optimization selects stationary beam positions to produce a both high quality and efficiently deliverable treatment plan, resulting in a large-scale combinatorial optimization problem. We study an efficient method for selecting beam orientations for IMRT treatments that explicitly incorporates the quality of the resulting optimal dose distribution.
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