2016 INFORMS Annual Meeting Program

MA19

INFORMS Nashville – 2016

4 - Robust Empirical Optimization Andrew Lim, National University of Singapore, Singapore, Singapore, andrewlim@nus.edu.sg, Jun-ya Gotoh, Michael Jong Kim We analyze the out-of-sample performance of robust empirical optimization and show that it asymptotically optimizes the mean-variance reward under the data generating model. We also introduce the notion of robust cross-validation as a method of calibrating the empirical robust optimization problem. Theoretical and experimental comparison to empirical optimization is also provided. MA17 105B-MCC Optimizing Power System Operations Under Uncertainty Sponsored: Optimization, Optimization Under Uncertainty Sponsored Session Chair: Yu Zhang, University of California, Berkeley, 200 California Hall, Albany, CA, 94720, United States, yuzhang49@berkeley.edu 1 - Unit Commitment Under Gas-supply Uncertainty And Gas-price Variability Antonio J. Conejo, The Ohio State University, 1971 Neil Av., Columbus, OH, 43210, United States, conejonavarro.1@osu.edu Ramteen Sioshansi, Bining Zhao We propose a two-stage stochastic optimization model to analyze the scheduling of power units under natural gas-supply uncertainty and natural gas-price variability. The first stage of this model represents the day-ahead scheduling stage, while the second stage represents real-time operations via scenarios. We use this model to analyze the effect of two types of gas-supply conditions. First, we analyze a case involving low-cost gas supply with gas-transmission issues. We then examine a case involving higher-cost gas supply, which is used solely to attain feasibility with fast ramping events. 2 - Stochastic Market Clearing With High-penetration Wind Power Integrating renewable energy into the modern power grid requires risk-cognizant dispatch of resources to account for the stochastic availability of renewables. Toward this goal, day-ahead stochastic market clearing with high-penetration wind energy is pursued in this work. The objective is to minimize the social cost, which consists of conventional generation costs, end-user disutility, and a risk measure of the system re-dispatching cost based on the conditional value-at-risk. The resulting convex optimization problem is solved via the sample average and the alternating direction method of multipliers. Numerical results corroborate the merits of the proposed approaches. 3 - Integrated Generator Maintenance And Operations Scheduling Under Uncertain Failure Times Beste Basciftci, Georgia Institute of Technology, Atlanta, GA, United States, beste.basciftci@gatech.edu Murat Yildirim, Shabbir Ahmed, Nagi Gebraeel In this study, we formulate an integrated generator maintenance and operations scheduling problem as a stochastic mixed integer program by considering unexpected failure times. We generate failure scenarios based on the remaining life time distributions of the generators. We adopt a scenario decomposition approach to solve this problem in a distributed framework that identifies and evaluates solutions by solving scenario subproblems. Finally, we present computational experiments demonstrating the effectiveness of the approach. MA18 106A-MCC Theory of Integer Optimization Sponsored: Optimization, Integer and Discrete Optimization Sponsored Session Chair: Alberto Del Pia, University of Wisconsin, Madison, Yu Zhang, University of California, Berkeley, yuzhang49@berkeley.edu, Georgios Giannakis

on structured and unstructured problems. 2 - Ellipsoidal Mixed-Integer Representability Jeffrey Poskin, University of Wisconsin - Madison, Wisconsin, WI, United States, poskin@wisc.edu, Alberto Del Pia Representability results for mixed-integer linear systems play a fundamental role in optimization since they give geometric characterizations of the feasible sets that can be formulated by mixed-integer linear programming. We consider a natural extension of mixed-integer linear systems obtained by adding just one ellipsoidal inequality. The set of points that can be described, possibly using additional variables, by these systems are called ellipsoidal mixed-integer representable. In this work, we give geometric conditions that characterize ellipsoidal mixed- integer representable sets. “ MA19 106B-MCC Evaluating the Performance of Optimization Solvers Sponsored: Computing Sponsored Session Chair: Paul Brooks, Virginia Commonwealth Univ, Virginia Commonwealth Univ, Richmond, VA, 00000, United States, jpbrooks@vcu.edu 1 - IPET: Interactive Performance Evaluation Tools For Benchmarking Optimization Software Gregor Hendel, ZIB, hendel@zib.de The optimization community has recently seen an increasing number of non- standard benchmark measures for evaluating solver performance, which some data processing tools do not deliver. The benchmark evaluation starting from the parsing of raw solver log data until the preparation of publication-ready tables can be time-consuming and error-prone if done by hand. In this talk, we will first review some of the specialized benchmark measures used for optimization software. We will then present a Python tool named IPET to speed-up repetitive tasks during benchmarking. We will give an overview of the tool and show examples how automated benchmark scripts can be created using IPET. 2 - Surrogate Models For Algorithm Configuration Meinolf Sellmann, IBM Research, Yorktown Heights, NY, United States, meinolf@us.ibm.com Automatic algorithm configurators are important tools for improving program performance. Local search approaches in particular have proven very effective for tuning. We study the use of non-parametric models in the context of population- based algorithm configurators. We introduce a model designed specifically for the task of predicting high-performance regions in the parameter space. Moreover, we introduce the ideas of genetic engineering of offspring. Numerical results show that model-based genetic algorithms significantly improve our ability to effectively configure algorithms automatically. 3 - Tuning Of Optimization Software Parameters For Mixed Integer Programming Problems Toni P Sorrell, Virginia Commonwealth University, Richmond, VA, United States, tpsorrel@vcu.edu, Paul Brooks, David Edwards The tuning of optimization software is of key interested to researchers solving mixed integer programming (MIP) problems because the efficiency of the optimization software is can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a model that would suggest settings of the parameters that provided the greatest impact on the primal integral. Primarily, this research focuses on using classes of MIPs to not only obtain good parameter settings for a practitioner to use on future instances of the same class of MIPs, but to also gain understanding of why the settings work well for that class of MIPs. 4 - Deploying MPL Optimization Models With Google Web Services API’s Bjarni Kristjansson, President, Maximal Software Inc., 2111 Wilson Boulevard, Suite 700, Arlington, VA, 22201, United States, bjarni@maximalsoftware.com Over the past decade the IT has been moving steadfastly towards utilizing software on clouds using Web Services API’s. The old standard way of deploying software on standalone computers is slowly going away. Google has been one of the leading software vendors in this area and publishes several web API’s which can be quite useful for deploying optimization applications. In this presentation we will demonstrate several Google API’s, including the Google Sheets API, Google Maps API, and Google Visualization API and show how they can be integrated with the MPL OptiMax Library for deploying optimization to service both web and mobile clients.

Madison, WI, United States, delpia@wisc.edu 1 - Facet Separation With One Linear Program

Laurence Wolsey, Université catholique de Louvain, laurence.wolsey@uclouvain.be, Michele Conforti

Given polyhedron P and and a point x*, the separation problem for polyhedra asks to certify that x* in P and if not, to determine an inequality that is satisfied by P and violated by x*. This problem is central in cutting plane methods for Integer Programming and the “quality” of the violated inequality is an essential feature in the performance of such methods. In this talk we address the problem of finding efficiently an inequality that is violated by x* and either defines an improper face or a facet of P. We provide some evidence that our method works

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