2015 Informs Annual Meeting

SD14

INFORMS Philadelphia – 2015

SD13 13-Franklin 3, Marriott Uncertainty in Energy and Natural Resource Systems Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Alexandra Newman, Professor, Colorado School of Mines, Mechanical Engineering, Golden, CO, 80401, United States of America, anewman@mines.edu 1 - Complementarity Effects Between Wind and Hydro in the Context of Power Generation Scheduling Anderson Rodrigo De Queiroz, Assistant Professor, Federal University of Itajubá, 1303 BPS Avenue, Itajubá, MG, 37500000, Brazil, ar_queiroz@yahoo.com.br, Saulo Ribeiro Silva, Luana Marangon Lima We present a computational model able to determine the optimal economic generation scheduling in a system with hydro, thermal and wind power plants. Our model takes into account the stochastic nature of wind speed and its complementarity with water inflows. We use sampling-based decomposition algorithms to solve such model and present results for a case study. Our analysis suggests that complementary is relevant and should be taken into consideration during the scheduling of generators. 2 - Global Thermal Coal Optimization under Uncertainty Ashley Arigoni, PhD Candidate, Colorado School of Mines, 1500 Illinois St, Golden, CO, 80401, United States of America, aarigoni@mymail.mines.edu Our objective is to minimize the cost to ship thermal coal to global fill demand while respecting import and export port capacities, ship size constraints, and coal specification requirements under spot market price uncertainty. Coal blending is allowed at demand nodes to meet coal specification requirements. We develop a two-stage stochastic model in which forward purchases are made before spot market uncertainties are realized. 3 - Mitigating Uncertainties with Stochastic Decomposition: Applications in Operations Management. Yifan Liu, University of Southern California, 3715 McClintock Ave, GER 240, Los Angeles, CA, 90089, United States of America, yifanl@usc.edu, Suvrajeet Sen Uncertainties are common in operation and production management. In this talk, we will discuss applying stochastic decomposition for solving a wide range of operations management applications including single period multi-products inventory problem, multi-location transhipment problem and process flexibility design problem. The first two problems are modeled as two-stage stochastic linear programs while the last one contains first stage binary variables. 4 - Quantifying Uncertainty in an Optimization Model using Exponential EPI-spline Density Estimation Michael Teter, Ltc, Colorado School of Mines, Golden, CO, 80401, United States of America, mteter@mymail.mines.edu Using a capital budgeting optimization model, we explore fusing hard and soft information through constrained nonparametric density estimation in order to quantify the uncertainty of the future. A comparison of solutions from data sets derived from traditional distributions and those derived from epi-splines demonstrate the benefits of nonparametric density estimation. These solutions prescribe a set of technologies in which the U.S. Army should invest to maximize effectiveness. SD14 14-Franklin 4, Marriott Topics in Dynamic Programming Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Francesca Maggioni, Assistant Professor, University of Bergamo, Via dei Caniana n 2, Bergamo, 24127, Italy, francesca.maggioni@unibg.it 1 - ADP for Risk-Averse Markov Decision Processes using Dynamic Quantile-Based Risk Measures Daniel Jiang, Princeton University, Sherrerd Hall, Charlton Street, Princeton, NJ, 08540, United States of America, drjiang@princeton.edu, Warren Powell We consider Markov decision process (MDP) for which the objective is to minimize a quantile-based risk measure of the sequence of future costs. In particular, we consider dynamic risk measures constructed using the one-step quantile (VaR) and the one-step conditional value at risk (CVaR). We propose simulation-based approximate dynamic programming (ADP) algorithms, modeled after Q-learning, and apply the algorithms in the context of an application in the energy market.

3 - Parallel Algorithms for MIP Feasibility Lluís Miquel Munguía, Georgia Institute of Technology, 266 Ferst Drive, Room 1343, Atlanta, GA, 30332, United States of America, lluis.munguia@gatech.edu, Shabbir Ahmed, David A. Bader, George L. Nemhauser, Yufen Shao We present highly parallelizable methods for obtaining feasible solutions to Mixed Integer Programming (MIP) instances. The use of several relaxations allows us to leverage parallelism to successfully obtain feasibile solutions to large-scale instances. We give computational results that demonstrate the effectiveness of the parallel algorithms. 4 - Facets for Continuous Multi-mixing Set with General Coefficients and Bounded Integer Variables Kiavash Kianfar, Associate Professor, Texas A&M University, TAMU 3131, College Station, TX, 77843-3131, United States of America, kianfar@tamu.edu, Manish Bansal Bansal and Kianfar developed facet-defining inequalities for continuous multi- mixing set where the coefficients satisfy certain conditions. We first generalize their inequalities for the continuous multi-mixing set with general coefficients and show that they are facet-defining in many cases. Next, we introduce a family of valid inequalities for the continuous multi-mixing set with general coefficients and bounded integer variables, and investigate their facet-defining properties. SD12 12-Franklin 2, Marriott Strong Relaxations and Computations for Mixed Integer Nonlinear Programs Sponsor: Optimization/Mixed Integer Nonlinear Optimization and Global Optimization Sponsored Session Chair: Jeff Linderoth, University of Wisconsin-Madison, 1513 University Avenue, Madison, WI, 53706-1572, United States of America, linderoth@wisc.edu 1 - Convex Hull of Two Quadratic or a Conic Quadratic and a Quadratic Inequality We consider an aggregation technique introduced by Yildiran [2009] to study the convex hull of regions defined by two quadratic or by a conic quadratic and a quadratic inequality. We show how this technique can be used to yield valid conic quadratic inequalities for the convex hull of sets defined by two quadratic or by a conic quadratic and a quadratic inequality. We also show that in many cases, these valid inequalities characterize the convex hull exactly. 2 - Relaxations and Heuristics for the General Multiple Nonlinear Knapsack Problem Luca Mencarelli, CNRS LIX, Ecole Polytechnique, Palaiseau, 91120, France, mencarelli@lix.polytechnique.fr, Claudia D’ambrosio, Silvano Martello We consider the multiple mixed-integer nonlinear knapsack problem. These problems are very difficult to solve, both from a theoretical and a practical viewpoint. We analyze different relaxations and extend known theoretical results for the multiple linear knapsack problem. Moreover, we propose fast constructive heuristic algorithms and a local search procedure. 3 - Recent Advances in CPLEX for Mixed Integer Nonlinear Optimization Pierre Bonami, IBM, Santa Hortensia 26, Madrid, Spain, pierre.bonami@es.ibm.com, Andrea Tramontani We present some of the new algorithmic techniques that have been recently added to the IBM CPLEX solver to address nonlinear optimization models. We focus in particular on mixed integer second order cone programming and quadratic optimization. We present extensive computational analysis to assess the performance gain from these techniques. 4 - Strong Convex Nonlinear Relaxations of the Pooling Problem Jeff Linderoth, University of Wisconsin-Madison, 1513 University Avenue, Madison, Wi, 53706-1572, United States of America, linderoth@wisc.edu, Jim Luedtke, Claudia D’ambrosio We investigate convex relaxations for the pooling problem. We characterize the extreme points of the convex hull of our non-convex set, and we derive valid nonlinear convex inequalities. Computational results demonstrate that the inequalities can significantly strengthen the convex relaxation of even the most sophisticated formulations of the pooling problem. Sina Modaresi, Discover/University of Pittsburgh, 2402 W Beardsley Road, Phoenix, AZ, United States of America, sim23@pitt.edu, Juan Pablo Vielma

123