2016 INFORMS Annual Meeting Program

MB18

INFORMS Nashville – 2016

MB18 106A-MCC Recent Advances in Theory and Applications of IPCO Sponsored: Optimization, Integer and Discrete Optimization Sponsored Session Chair: Manish Bansal, Northwestern University, C234 Technological Institute, 2145 Sheridan Road, Evanston, IL, 60202, United States, manish.bansal@northwestern.edu 1 - Decomposition For Loosely Coupled Mixed-integer Programs: A Multiobjective Perspective Merve Bodur, Georgia Institute of Technology, merve.bodur@gatech.edu, Natashia Boland, Shabbir Ahmed, George L Nemhauser We consider loosely coupled mixed-integer programs (MIPs), that consist of (possibly a large number of) interrelated subsystems and a small number of constraints, which link blocks of variables that correspond to different subsystems. Motivated by recent developments in multi-objective programming (MOP), we develop a MOP-based branch-and-price algorithm to solve loosely coupled MIPs. We discuss the similarities and differences of our algorithm with the traditional branch-and-price algorithm. Also, we present computational results on instances with knapsack structure in the subsystems. 2 - Maximum Demand Rectangular Location Problem Manish Bansal, Northwestern University, Evanston, IL, United States, manish.bansal@northwestern.edu, Kiavash Kianfar We introduce a new generalization of the classical planar maximum coverage location problem by positioning a given number of rectangular service zones (SZs) on the 2-D plane to cover a set of existing (possibly overlapping) rectangular demand zones such that the total covered demand is maximized. We refer to this problem as Maximum Demand Rectangular Location Problem (MDRLP) which also has application in camera-frame selection for telerobotics. We present an improved algorithm for the single-SZ MDRLP, which is at least two times faster than the existing exact algorithm. We then provide theoretical properties for multi-SZ MDRLP and an exact algorithm to solve it along with our computational results. 3 - Cutting Planes fromMultiple-term Disjunctions Egon Balas, Carnegie Mellon University, eb17@andrew.cmu.edu (Less than 600 characters excluding spaces): Lift-and-project cuts from split disjunctions have their counterpart as intersection cuts from a (feasible or infeasible) LP tableau, and thus can be generated by pivoting in the latter. This correspondence breaks down in the case of general disjunctions: here the bases of the CGLP and associated L&P cuts can be either regular or irregular. Irregular cuts do not correspond to intersection cuts and cannot be obtained by pivoting in the LP tableau; they tend to be more numerous and stronger than regular cuts. Some irregular L&P cuts can be generated without recourse to a higher dimensional CGLP, as generalized intersection cuts from the disjunction underlying the L&P cut. MB19 106B-MCC Models and Methods for Large-Scale Mixed-Integer Optimization Sponsored: Computing Sponsored Session Chair: Simge Kucukyavuz, Ohio State University, Ohio State University, Columbus, OH, United States, kucukyavuz.2@osu.edu 1 - Two-stage Stochastic Programming Models Under Multivariate Risk Constraints In this study, we consider multicriteria risk-averse two-stage stochastic programming problems. The aim is to find the best decision for which the associated random outcome vector of interest is preferable to a specified benchmark with respect to the multivariate polyhedral conditional value-at-risk relation. In this case, classical decomposition methods can not be used due to complicating risk constraints. We propose an exact solution algorithm based on Benders decomposition and show its convergence. Computational experiments are performed on a disaster relief network design problem. Merve Merakli, Ohio State Universtity, Columbus, OH, United States, merakli.1@osu.edu, Simge Kucukyavuz, Nilay Noyan

2 - Chance-constrained Stochastic Programming Under Variable Reliability Levels With An Application To Humanitarian Relief Network Design Özgün Elçi, Sabanci University, Istanbul, 34956, Turkey, nnoyan@sabanciuniv.edu, Nilay Noyan, Kerem Bulbul A recently introduced class of models treats reliability levels associated with chance constraints as decision variables and trades off the actual cost against the cost of the selected reliability levels. Leveraging recent methodological advances for solving chance-constrained linear programs with fixed reliability levels, we develop strong MIP formulations for this new variant with variable reliability levels. In addition, we introduce an alternate cost function type associated with the reliability levels which requires capturing the value-at-risk associated with a variable reliability level. We apply the proposed modeling approach to a new humanitarian relief network design problem. 3 - Bilevel Risk Averse Formulations Of Stochastic Programming Problems Deniz Eskandani, Rutgers University, 100 Rockafeller Rd, Piscataway, NJ, 08854, United States, deniz.eskandani@rutgers.edu Jonathan Eckstein We describe a bilevel programming technique to time-consistently formulate 3- stage stochastic programs without using nested risk measures. For some classes of applications, we empirically demonstrate that its behavior can be dramatically different from standard formulations. 4 - Irreducible Infeasible Subsystem Decomposition For Stochastic Integer Programs With Probabilistic Constraints Lewis Ntaimo, Associate Professor, Texas A&M University, College Station, TX, United States, ntaimo@tamu.edu Bernardo Pagnoncelli Probabilistically constrained stochastic integer programs (PC-SIPs) are very challenging to solve and linear programming (LP) provides very weak bounds on the optimal value. This work considers a decomposition approach using irreducible infeasible subsystem (IIS) inequalities for strengthening the LP- relaxation of PC-SIPs. Preliminary computational results will be presented. Chair: Nicholas G Hall, Ohio State University, 658 Fisher Hall, 2100 Neil Avenue, Columbus, OH, 43210-1144, United States, hall.33@osu.edu 1 - Research And Teaching Opportunities inProject Management Nicholas G Hall, Ohio State University, 658 Fisher Hall, 2100 Neil Avenue, Columbus, OH, 43210-1144, United States, hall.33@osu.edu One-fifth of the world’s economic activity, with an annual value of $12 trillion, is organized using the business process of project management. This process has exhibited dramatic growth in business interest in recent years, with a greater than 1000% increase in Project Management Institute membership since 1996. Contributing to this growth are many new applications of project management. These include IT implementations, research and development, software development, corporate change management, and new product and service development. However, the very different characteristics of these modern projects present new challenges. The partial resolution of these challenges within project management practice over the last 20 years defines numerous interesting opportunities for academic researchers. These research opportunities make use of a remarkably broad range of methodologies, including robust optimization, cooperative and noncooperative game theory, nonlinear optimization, predictive analytics, empirical studies, and behavioral modeling. Furthermore, the $4.5 trillion that is annually at risk from a shortage of skilled project managers, and the 15.7 million new jobs in project management expected by 2020, provide great opportunities for contributions to project management education. These educational opportunities include the integration of case studies, analytics challenges, online simulations, in-class games, self-assessment exercises, videos, and guest speaker presentations, which together form an appealing course for both business and engineering schools. MB20 106C-MCC Research and Teaching Opportunities in Project Management Invited: Tutorial Invited Session

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