2015 Informs Annual Meeting

TC19

INFORMS Philadelphia – 2015

2 - s-plex and s-defective Numbers of a Graph Vladimir Stozhkov, University of Florida, 2330 SW Williston Rd Apt. 2826, Gainesville, FL, 32608, United States of America, vstozhkov@ufl.edu, Eduardo Pasiliao, Vladimir Boginski The presentation is dedicated to two clique relaxation models: s-plex and s- defective clique. Theoretical properties of the specified objects are investigated. Analytical and computational bounds for the related optimization problems are provided. The extensions of the Motzkin-Straus formulation for s-plex and s- defective clique are derived. The outline of the general procedure for solving the corresponding maximization problems is given. 3 - Minimum Edge Blocker Dominating Set Problem Foad Mahdavi Pajouh, Assistant Professor, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA, 02125, United States of America, Foad.Mahdavi@umb.edu, Eduardo Pasiliao, Jose Walteros, Vladimir Boginski Dominating sets are widely used in social and communication networks analysis. Given a weighted graph and r>0, we consider the problem of removing a minimum number of edges so that the weight of any dominating set in the remaining graph is at least r. Complexity results, polyhedral results, a linear 0-1 programming formulation, and an exact algorithm for solving this problem will be presented. 4 - Minimum Risk Network Covering Location Problem Konstantin Pavlikov, University of Florida, 1350 N. Poquito Road, Shalimar, FL, 32579, United States of America, kpavlikov@ufl.edu, Alexander Veremyev, Vladimir Boginski, Eduardo Pasiliao The network coverage problem under uncertainty is considered. In this problem, components of the covering set and links connecting them to remaining nodes of the network are subject to random failures. The emphasis is put on minimizing the risk of losing coverage in presence of such failures. We formalize the model and discuss its connection to the maximum expected covering location model. Chair: Yunpeng Pan, South Dakota State University, Mathematics & Statistics, Box 2220, Brookings, SD, 57007, United States of America, yunpeng.pan@gmail.com 1 - Remote Sensing Data Mining for Extracting Data Center Site Characteristics Yunpeng Pan, South Dakota State University, Mathematics & Statistics, Box 2220, Brookings, SD, 57007, United States of America, yunpeng.pan@gmail.com, Adam Buskirk Data centers are powerhouses of cloud. Companies rush to build out their cloud infrastructure to meet fast growing demand. The environmental impact such as carbon footprint falls into the category of public good, and therefore, calls for appropriate public policy decisions, which in turn require good information. Our current work intends to achieve this by mining the Landsat remote sensing data to extract characteristics of data centers in operation and under construction at a global scale. 2 - A Dynamic Workflow Framework for Server Provisioning Wei Lin, Software Engineering Researcher, IBM, 8 Dongbeiwang Western Road, Haidian Dist, Beijing, China, linweilw@cn.ibm.com, Brian Peterson, Qinhua Wang, Zongying Zhang, Christopher Young, Sai Zeng Cloud service providers support server provisioning to large number of enterprise customers, who have different functional, security and compliance requirements. We propose a framework which composes dynamic workflow at runtime to cater individualized provisioning procedures. In this framework, an onboarding module configures process steps and dependencies for each customer, and a composition module dynamically composes execution workflow based on dependency validation and sequence calculating. 3 - Minimizing Costs in Distributed Cloud Resource Provisioning Julio Goez, Postdoctoral Fellow, Ecole Polytechnique Montreal and GERAD, 2900 Boulevard Edouard-Montpetit, Montréal, QC, H3T 1J4, Canada, jgoez1@gmail.com, Juan F. Pérez We consider the problem of minimizing the cost of provisioning resources at different cloud locations, constrained to satisfying a required service-level objective. We present a mixed integer non-linear optimization model for this problem and show an equivalent mixed integer second order cone formulation. We also show that a simple round-up provides an initial feasible solution for the TC19 19-Franklin 9, Marriott Modeling and Optimization for Sustainable Cloud Computing Sponsor: Computing Society Sponsored Session

problem. We use this property to design a heuristic procedure to improve the quality of the initial solution. 4 - Renewable Energy Prediction and Prescription in the Internet-of-things (IoT) Hans Schlenker, IBM, Hollerithstr 1, Munich, 81829, Germany, hans.schlenker@de.ibm.com, Yianni Gamvros The IoT connects all sorts of devices — from sensors to embedded devices to smartphones to laptops to servers. IBM connected 1600 solar fields to its Renewable Energy IoT. Sensor data is collected, combined in the cloud, and further analyzed by analytics services to generate accurate local energy production forecast. These predictions are then used by (prescriptive) mathematical optimization in a network distribution model to balance under-runs and over-production in all connected areas.

TC20 20-Franklin 10, Marriott Financial Engineering and Optimization Contributed Session

Chair: Zhen Liu, Options Clearing Corp (OCC), One North Wacker Drive, Suite 500, Chicago, IL, 60606, United States of America, zhenliu@alum.northwestern.edu 1 - An Optimization Procedure for a Delta Neutral Constrained Theta with Maximum Gamma Portfolio A large gamma portfolio is attractive for investors in order to get benefits from large increase or decrease in the value of the underlying asset. In large gamma portfolio the theta is negative. In this study, a delta neutral portfolio with maximum gamma and constrained theta, was developed. An optimization model was designed and solved for small time steps within a planning horizon. The model was run for many simulation scenarios as well as real world data, followed by statistical tests. 2 - Optimal Portfolio Liquidation and Dynamic Mean-variance Criterion Jiawen Gu, Postdoc, University of Copenhagen, Department of Mathematical Science, University of Copenhagen, Copenhagen, 2100, Denmark, kaman.jwgu@gmail.com, Mogens Steffensen We consider the portfolio liquidation problem under the dynamic mean-variance criterion and derive time-consistent solutions in three important models. We get explicit trading strategies in the basic model and when random pricing signals are incorporated. When consider stochastic liquidity and volatility, we construct an exact HJB equations under general assumptions for the parameters. 3 - Genetic Programming Optimization for a Sentiment Feedback Strength Based Trading Strategy Steve Yang, Assistant Professor, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ, 07030, United States of America, steve.yang@stevens.edu Based on the evidence that tweets are faster than news in revealing new market information, whereas news is regarded broadly a more reliable source of information than tweets, we develop a trading strategy based on the sentiment feedback strength between the news and tweets using generic programming optimization method. Result shows that this strategy generates over 14.7% Sterling ratio compared with 10.4% and 13.6% from the technical indicator- based and the buy-and-hold strategy respectively. 4 - Algorithmic Options Trading by Integer Programming Vadim Timkovski, Keiser University, Port St. Lucie, FL, United States of America, vtimkovski@keiseruniversity.edu Algorithmic options trading has only begun its evolution. This work presents an integer programming system that simulates the activities of an experienced option trader on the construction and adjustment of option portfolios. The system adopts algorithms based on a recent discovery of an algebraic classification of option trading strategies, without which this kind of automation would not be possible and which has not been considered before as attainable. 5 - Linear Programming Approach to American Option Pricing Zhen Liu, Options Clearing Corp (OCC), One North Wacker Drive, Suite 500, Chicago, IL, 60606, United States of America, zhenliu@alum.northwestern.edu We solve the variational inequality (VI) from American option pricing problem by linear programming (LP) approach. We approximate its solution by a combination of Chebyshev basis functions. The objective is to minimize the absolute error of the solution and the max operator in VI is converted into linear constraints of LP. We discuss its convergence, and compare our results with Longstaff-Schwartz least-square approach and numerical partial differential equation (PDE) approach. Arik Sadeh, Dean, HIT Holon Institute of Technology, 52 Golomb St. 5810201, Holon, Israel, sadeh@hit.ac.il

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