2015 Informs Annual Meeting

WE15

INFORMS Philadelphia – 2015

2 - Novel Sampling Technique for High Dimensional Stochastic Optimization Problem Nishant Dige, Graduate Student Industrial Engineering, University of Illinois at Chicago, 1200 W Harrison Street, Chicago, IL, 60607, United States of America, ndige2@uic.edu, Urmila Diwekar Computational speed is critical in optimizing large scale stochastic problems and the major bottleneck is the computational intensity of samples. For sampling randomness is important but uniformity governs its accuracy. This paper presents a novel sampling approach; combining LHS & Sobol Sampling for better uniformity in single as well as multi dimensions & also to avoid clustering effect for higher dimensions. We have implemented this technique on stochastic supply chain network design problem. 3 - Inventory Management Based on Target-oriented Robust Optimization Yun Fong Lim, Associate Professor, Singapore Management University, 50 Stamford Road, #04-01, Singapore, 178899, Singapore, yflim@smu.edu.sg, Chen Wang We propose a target-oriented robust optimization approach to solve a multi- product, multi-period inventory problem subject to capacity constraints. The product demands are characterized by uncertainty sets. We find an ordering policy that maximizes the uncertainty sets such that all demand realizations from the sets result in a cost lower than a pre-specified target. We prove that a static policy is optimal and it can achieve a balance between the expected cost and the associated cost variance. 4 - Optimal Learning of Demand for The Nested Lagged Commitment Problem We address the problem of making lagged commitments to resources in order to maximize revenue over time while sequentially making decisions. The motivating application is hotel resource management and a separate dimension involves learning how the market will respond to price. We consider two cases: where demand is unknown but static and where demand is unknown and dynamic. We use the optimization algorithm called the Knowledge gradient to learn the optimal demand function. 5 - Continuity of Robust Optimization Problems with Respect to the Uncertainty Set Nana Aboagye, PhD Candidate, Princeton University, Sherrerd Hall, Charlton Street, Princeton, NJ, 08544, United States of America, aboagye@princeton.edu, Warren Powell We discuss the stability properties of robust problems satisfying the Strong Slater condition, with respect to their uncertainty sets. We show, by way of results in Linear Semi-Infinite Optimization, that the optimal values of the robust optimization problem are Lipschitz continuous with respect to the Hausdorff distance between their respective uncertainty sets. We also present implications for measuring a price of robustness and approximating robust optimization with complex uncertainty sets. WE14 14-Franklin 4, Marriott Risk-Aware Decision Making under Uncertainty Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Ruiwei Jiang, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109, United States of America, ruiwei@umich.edu 1 - A Composite Risk Measure Framework for Decision Making under Uncertainty Pengyu Qian, Columbia University, Columbia Business School c/o PhD Office, 3022 Broadway,311 Uris Hall, New York, NY, 10027, United States of America, qianpengyu@pku.edu.cn, Zaiwen Wen, Zizhuo Wang In this talk, we present a unified framework for decision making under uncertainty. Our framework is based on the composite of two risk measures accounting for parametric (given distribution) and distributional uncertainty respectively. The framework generalizes many existing models. We also propose new models within this framework whose solutions have probabilistic guarantees and are less conservative comparing to traditional models. Numerical experiments demonstrate the strength of our models. Philip Allen Mar, Dept. of MIE, University of Toronto, 5 King’s College Road, Toronto, ON, M5S 3G8, Canada, philip.mar@mail.utoronto.ca, Timothy Chan

2 - Risk-averse Two-stage Stochastic Program with Distributional Ambiguity Ruiwei Jiang, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109, United States of America, ruiwei@umich.edu, Y ongpei Guan We develop a risk-averse two-stage stochastic program (RTSP) taking into account the distributional ambiguity. We derive an equivalent reformulation for RTSP that applies to both discrete and continuous distributions. Also, the reformulation reflects its linkage with a full spectrum of coherent risk measures under varying data availability. 3 - Risk Sharing in Classification Problems Constantine Vitt, PhD Candidate, Rutgers University, 1 Washington Park, Newark, NJ, 07102, United States of America, constantine.vitt@rutgers.edu, Darinka Dentcheva, Hui Xiong We develop a new approach to solving classification problems based on the theory of coherent measures of risk and risk sharing. We view labeled training data as random samples from populations with unknown distributions, subject to change. The key idea of the proposed methodology is to associate individual measures of risk with the misclassification of each class. We analyze the problem theoretically and propose a numerical method to identify the proper risk sharing among the classes. Chair: Vahid Nourbakhsh, PhD Student, UC Irvine, The Paul Merage School of Business, The Paul Merage School of Business, University of California-Irvine, Irvine, CA, 92697, United States of America, vahidn@uci.edu 1 - Reliability Optimization for Multi-components System in the Design Phase Qianru Ge, PhD Candidate, Technology University of Eindhoven, Paviljoen E.03, IE&IS, Eindhoven, 5612 AZ, Netherlands, q.ge@tue.nl We develop an optimization model to determine the optimal failure rate of critical components in a system. Since the system is under a service contract, a penalty cost should be paid by the OEM when the total system down time exceeds a predetermined level, which complicates the evaluation of the life cycle costs. Furthermore, in the design phase for each critical component, the failure rate can be chosen from a certain range. 2 - Multistage News Vendor Problem with Targets Vishwakant Malladi, Doctoral Student, UT Austin, Austin, TX, 78703, United States of America, Vishwakant.Malladi@phd.mccombs.utexas.edu We analyze the optimal control policy of a multi-stage new vendor problem with targets. The results show a tractable and intuitive control policy for each stage. 3 - Efficient Methodology to Maximizing Total Noise Reduction and Minimize Total Cost in Traffic Design Golshan Madraki, PhD Candidate, Ohio University, 15 Station St. Apt F, Athens, Oh, 45701, United States of America, g.madraki@gmail.com Three crucial variables that affect the efficiency of a Traffic noise barrier are: the distance from receivers, height of the barrier and the material of the barrier. A novel combination of methodologies is proposed to maximize the efficiency of the barrier with minimum cost. An example model of the barriers are simulated by TNM2.5 software used to perform Factorial design of experiment to fit a meta- model. LP-metric method is applied to solve the multi-objective math-model. 4 - On Solving Bi-level Programming Problem with Fuzzy Random Variable Coefficients Vishnu Pratap Singh, Research Scholar, Indian Institute of Technology Kharagpur, Department of Mathematics, Kharagpur, 721302, India, vishnupratapsingh56@gmail.com, Debjani Chakraborty This paper represents the bi-level linear programming problem in an imprecise and uncertain mixed environment. The aim of this paper is to introduce leader and the follower’s demand as fuzzy random variable. To determine the optimal value of the leader and follower’s objectives a new methodology is developed for bi-level linear programming model in presence of fuzzy random variable. A numerical example is solved to demonstrate the methodology. WE15 15-Franklin 5, Marriott Optimization Methodology II Contributed Session

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