2016 INFORMS Annual Meeting Program

SA07

INFORMS Nashville – 2016

SA05 101E-MCC Power System Resilient Design and Optimization Sponsored: Energy, Natural Res & the Environment, Energy I Electricity Sponsored Session Chair: Seyedamirabbas Mousavian, Clarkson University, 8 Clarkson Avenue, Potsdam, NY, 13699-5790, amir@clarkson.edu 1 - Self-healing Attack-resilient PMU Network For Power System Operation Chen Chen, Argonne National Laboratory, Lemont, IL, United States, morningchen@anl.gov, Hui Lin, Jianhui Wang, Junjian Qi, Dong Jin We propose a self-healing PMU network by exploiting the features of programmable configuration in a software-defined networking (SDN) to achieve resiliency against cyber-attacks. After a cyber-attack, by changing the configuration of the network switches, the disconnected yet uncompromised PMUs will be reconnected to the network to “self-heal” and thus restore the observability of the power system. Specifically, we formulate an integer linear programming (ILP) model to minimize the overhead of the self-healing process, while considering the constraints of power system observability, hardware resources, and network topology. 2 - Minimum Risk-maximum Availability Response To Electric Vehicle-initiated Smart Grid Attacks Seyedamirabbas Mousavian, Clarkson University, amir@clarkson.edu, Melilke Erol-Kantarci, Thomas Ortmeyer Malware pose a significant threat to the power grid and the connected electric vehicle infrastructure. Penetration and propagation of cyber attacks including worms and viruses vary depending on the nature of the connected systems. Electric vehicles (EVs) being the mobile portion of the smart grid may easily spread worms and viruses in a large geographic area. We propose a probabilistic model for the worm propagation in EV to Electric Vehicle Supply Equipment (EVSE) networks, formulate threat levels and then, we propose a Mixed Integer Linear Programming (MILP) model as a protection scheme that relies on isolating infected nodes. 3 - Storage And Generation Expansion Problem Considering Primary Response A sustainable and efficient generation and storage expansion program needs to consider both the capacity needs and short-term operational requirements of a power system. A generation expansion formulation considering frequency regulation using both generators and storage units will be presented. 4 - Optimal Resilient Grid Design Distribution And Transmission Systems Russell Bent, Los Alamos National Laboratory, rbent@lanl.gov Emre Yamangil, Harsha Nagarajan, Pascal Van Hentenryck Modern society is critically dependent on the services provided by engineered infrastructure networks, particularly distribution and transmission grids. When natural disasters (e.g. Hurricane Sandy) occur, the ability of these networks to provide service is often degraded. However, well-placed upgrades to these grids can greatly improve post-event network performance. Hence, we pose the optimal electrical grid resilient design problem as a two-stage, stochastic mixed- integer program with damage scenarios and propose decomposition-based algorithms to solve and analyze medium-sized networks. SA06 102A-MCC Joint Session DM/Optimization: Discrete Optimization and Machine Learning Sponsored: Data Mining Sponsored Session Chair: Berk Ustun, Massachusetts Institute of Technology, Cambridge, MA, United States, ustunb@mit.edu 1 - A Multi-group Discrete Support Vector Machine – Theory And Computation Eva Lee, Georgia Tech, evakylee@isye.gatech.edu We describe a general-purpose machine learning framework, DAMIP, for discovering gene signatures that can predict vaccine immunity and efficacy. DAMIP is a multi-group ‘concurrent’ classifier that offers unique features not Hrvoje Pandzic, University of Zagreb, Zagreb, Croatia, hrvoje.pandzic@fer.hr, Yury Dvorkin, Miguel Carrion

present in other models: a nonlinear data transformation to manage the curse of dimensionality and noise; a reserved-judgment region that handles fuzzy entities; and constraints on the allowed percentage of misclassifications.Computational results for biological and medicine problems will be discussed. 2 - Optimized Risk Scores Berk Ustun, MIT, Massachusetts Institute of Technology, Sloan School of Management, Cambridge, MA, 02142, United States, ustunb@mit.edu, Cynthia Rudin Risk scores are simple models that let users quickly assess risk by adding, subtracting, and multiplying a few small numbers. These models are widely used in healthcare and criminology, but difficult to create because they need to be risk- calibrated, use small integer coefficients, and obey operational constraints. We present a new approach to learn risk scores from data by solving a discrete optimization problem. We formulate the risk score problem as a MINLP, and present a cutting-plane algorithm to efficiently recover the optimal solution by solving a MIP. We demonstrate the benefits of our approach by creating risk scores for real world problems. 3 - Supersparse Integer Regression Model For Nonparametric Failure Time Analysis Keivan Sadeghzadeh, MIT Sloan School of Management, Cambridge, MA, United States, keivan@mit.edu, Cynthia Rudin Analysis of failure time data has an inevitable role in predicting events occurrence. We develop an integer-based predictive model that is accurate and also interpretable, in order to determine effective features and predict potential failures. The strategy is to select appropriate covariates for censored large-scale and high-dimensional failure time data in a regression model. Our approach is to design robust algorithm to find the optimal integer solution for supersparse linear model. This optimal solution is reached by using machine learning techniques over a high-dimensional closed quadric hypersurface. 4 - Nested Clustering On A Graph Gokce Kahvecioglu, Northwestern University, 2145 Sheridan Road We study a clustering problem defined on an undirected graph with weight function defined on the edges, which denotes the importance of the connection between vertices. We remove a set of edges in order to maximize the number of clusters in the residual graph while minimizing the weight of deleting edges. Solving this graph clustering problem parametrically identifies the solutions that lie on the concave envelope of efficient frontier and the breakpoints on this envelope have a nested structure. We propose to solve this parametric model in polynomial time by solving a sequence of parametric maximum flow problems, which yields the family of nested clusters on the efficient frontier. Room C210, Evanston, IL, 60208, United States, gokcekahvecioglu2014@u.northwestern.edu David P. Morton Chair: Pavithra Harsha, IBM Research, 1101 Kitchawan Road, Room 34-225, Yorktown Heights, NY, 10598, United States, pharsha@us.ibm.com 1 - Car Sharing Fleet Location Design With Mixed Vehicle Types For CO2 Emission Reduction Joy Chang, University of Michigan, Ann Arbor, MI, United States, joychang@umich.edu. Siqian Shen, Ming Xu As carsharing companies integrate electric vehicles to reduce CO2 emissions, we optimize a mixed-integer linear program to study a carsharing fleet location design problem with mixed vehicle types. We create a minimum cost flow formulation on a spatial-temporal network to model round-trip and one-way vehicle flows. We test different one-way trip demands, and provide a model that ensures the first-come first-serve principle while satisfying CO2 restrictions. Via testing instances from the Boston Zipcar fleet, we provide insights on carsharing fleet location and vehicle-type composition. 2 - Collaborative Decision Making For Air Traffic Management Hale Erkan, Bilkent University, Ankara, Turkey, hale.erkan@bilkent.edu.tr, Nesim K. Erkip, Ozge Safak We propose a model which can be utilized within a collaborative decision making (CDM) framework for rescheduling of flights. The proposed mathematical program is expected to be utilized by major stakeholders, airlines and air navigation service providers. After providing the constraints, we list possible equity and efficiency performance measures that will make-up the objective function to be used by a stakeholder. We suggest guidelines to utilize the model for any stakeholder within CDM. Finally, a case study is prepared using publicly available data to demonstrate possible benefits. SA07 102B-MCC Undergraduate OR Prize – I Invited: Undergraduate Operations Research Prize Invited Session

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