2015 Informs Annual Meeting

TC13

INFORMS Philadelphia – 2015

2 - Facility Location Problem with Appointment Scheduling in Healthcare Mengnan Chen, University of Central Florida, Department of IEMS, Orlando, FL, 32816, United States of America, cmn891127@knights.ucf.edu, Qipeng Zheng Facility location problem with appointment scheduling in healthcare is used to schedule elective surgeries for physician, as well as to provide multiple choices for patients. Facility location problem aims to improve the match between healthcare resources (physician, clinic location) and patient needs (preferences and types of diseases). By solving this problem, we can meet the minimum of loss for the hospital (the total travel time) and satisfied the each patient preference as much as possible. 3 - Exploiting Variability with Machine Learning Based Restart Strategies for MIP Solvers Lars Beckmann, University of Paderborn, Warburger Strafle 100, Paderborn, De, 33100, Germany, lars.beckmann@gmail.com Performance variability, a.k.a. the heavy-tail phenomenon, can greatly affect the solution time of combinatorial problems, especially mixed-integer programming (MIP) models. We present a machine learning based restart strategy that exploits variability for quickly solving MIP problems. We show that this approach is effective on a large dataset of MIP models. Our approach can be integrated in any MIP solver and can potentially be generalized for solving other combinatorial problems as well. 4 - Faster Infeasibility Analysis for Mixed Integer Linear Programming John Chinneck, Professor, Carleton University, Systems and Computer Engineering, 1125 Colonel By Drive, Ottawa, ON, K1S5B6, Canada, chinneck@sce.carleton.ca, Andrew Scherr Finding an Irreducible Infeasible Subset of Constraints in an infeasible mixed integer linear program is extremely time consuming due to the need to solve many different MILPs. We present a number of new algorithms that greatly speed the process of analyzing infeasibility by reducing the number and size of MILP subproblems solved. 5 - Algorithms for Determining Internal Credit Ratings Srinivas Bollapragada, Chief Engineer, General Electric, 1 Research Circle, Niskayuna, NY, 12309, United States of America, bollapragada@ge.com, Xing Wang Financial Institutions assign credit ratings to customers for managing risks. Underwriters use these ratings to determine risk-based pricing. We developed an efficient algorithm to assign credit ratings to customers. Our algorithm partitions the probability of defaults associated with customers into a specified number of risk classes to achieve a target mean probability of default for each class. GE Capital Services uses our algorithm for its risk management needs. Stochastic Combinatorial Optimization Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Sean Skwerer, Yale, 300 George Street, Suite 523, New Haven, CT, United States of America, sean.skwerer@yale.edu 1 - Finite Horizon Markov Decision Problems and a Central Limit Theorem for Total Reward Alessandro Arlotto, Duke University, Durham, NC, United States of America, aa249@duke.edu, J. Michael Steele We prove a CLT for a class of additive processes that arise naturally in the theory of finite horizon Markov decision problems (MDPs). The theorem generalizes a result of Dobrushin for temporally non-homogeneous Markov chains. The innovation is that here the summands are permitted to depend on the current state and a bounded number of future states of the chain. We show through examples that this flexibility gives a direct path to asymptotic normality of the total reward of finite horizon MDPs. 2 - Simplifying Ensembles of Trees Sean Skwerer, Yale, 300 George Street, Suite 523, New Haven, CT, United States of America, sean.skwerer@yale.edu Recursive partition functions (RPFs) are used in a wide variety of statistical methods including classification and regression trees, regression splines, random forests and boosting. The focus of this research is to find a framework for aggregating an ensemble into a single tree or a small number of trees which have comparable predictive strength to the entire ensemble. I will discuss advances made in aggregating ensembles of recursive partition functions. TC13 13-Franklin 3, Marriott

3 - Large Submatrix Detection in Gaussian Random Matrices Quan Li, Massachusetts Institute of Technology, 550 Memorial Drive Apt. 12B4, Cambridge, MA, 02139, United States of America, quanli@mit.edu, David Gamarnik Iterative Search Algorithm is widely used to find large average submatrices of a real-valued matrix in the exploratory analysis. It alternately updates rows and columns until no further improvement is obtained. We present first theoretical analysis of its performance in Gaussian random matrices. We show that the algorithm terminates within finite iterations w.h.p.. This result implies that it converges to a local maximum submatrix w.h.p., leading to a constant factor gap from the global maximum. 4 - Locality in Optimization Patrick Rbeschini, Yale University, New Haven, CT, United States of America, Patrick.rebeschini@yale.edu, Sekhar Tatikonda How does the solution of a constrained network optimization problem behave under perturbations of the constraints with respect to the topology of the network? Typically, sensitivity results concern the objective function evaluated at the optimal point, not the optimal point itself. We develop a general theory for the local sensitivity of optimal points on networks. In the context of the network flow problem, this theory yields that local perturbations on the constraints have an impact on the components of the optimal point that decreases exponentially with the graph-theoretical distance. These results suggest a notion of decay of correlation for (non-random) optimization procedures. This notion can be used to develop local algorithms that can substantially reduce the computational complexity of canonical optimization procedures. TC14 14-Franklin 4, Marriott Stochastic Financial Optimization Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session 20052, United States of America, mlejeune@gwu.edu 1 - Scenario Decomposition for a Class of Risk-averse Stochastic Programs Pavlo Krokhmal, University of Iowa, 2136 Seamans Center, Iowa City, IA, United States of America, krokhmal@engineering.uiowa.edu We consider nonlinear convex stochastic optimization problems where objective or constraints involve downside coherent or convex risk measures of special form. A scenario decomposition algorithm that exploits the constraint structure induced by the corresponding risk measures is proposed. Numerical experiments on portfolio optimization problems illustrate the computational effectiveness of the developed procedure. 2 - Risk-budgeting Multi-portfolio Optimization with Portfolio and Marginal Risk Constraints Ran Ji, PhD Candidate, The George Washington University, 2201 G St, NW, Funger 415H, Washington, DC, 20052, United States of America, jiran@gwmail.gwu.edu, Miguel Lejeune We propose several stochastic risk budgeting multi-portfolio optimization models with portfolio and marginal risk constraints. The models permit the simultaneous optimization of multiple sub-portfolios with a downside risk measure assigned to each asset and sub-portfolio. Each model includes a joint chance constraint with random technology matrix. We expand a combinatorial modeling framework to represent the feasible set of the chance constraint as a set of mixed-integer linear inequalities. 3 - Factor-based Robust Indexing Roy Kwon, Associate Professor, University of Toronto, 5 King’s College Road, Toronto, Canada, rkwon@mie.utoronto.ca, Dexter Wu We present an approach for tracking a benchmark index using robust factor models. Robust versions of the Fama-French 3 and 5 factor models are developed to construct uncertainty sets for a robust conic integer program. Constraints limit risk, tracking error, and number of tickets. A Lagrangian-based strategy is developed and computational results in tracking the SP 100 and SP 500 show that robust indexing can offer enhanced indexation in turbulent market conditions. Chair: Miguel Lejeune, Associate Professor, The George Washington University, 2201 G Street, NW, Funger Hall, Suite 415, Washington, DC,

322