2015 Informs Annual Meeting

WA73

INFORMS Philadelphia – 2015

3 - Optimization of Active Traffic Management Strategies using Mixed Integer Linear Programming Joseph Trask, North Carolina State University, 909 Capability Dr, Raleigh, NC, 27606, United States of America, jltrask@ncsu.edu, John Baugh, Nagui Rouphail, Behzad Aghdashi In order to reduce congestion and improve travel times Active Traffic Management (ATM) strategies are often used to better control flow on freeways. This work presents a Mixed Integer Linear Programming formulation for the oversaturated method of the Highway Capacity Manual. The model is then used to optimize and provide insight into the usage of ATM strategies such as hard shoulder running and ramp metering. 4 - Constrained Optimization for the Morning Commute Problem with Flat Toll and Nonidentical Commuters Xiaolei Guo, Associate Professor, University of Windsor, 834 Hacienda Court, Odette School of Business, Windsor, Canada, guoxl@uwindsor.ca, Da Xu We study the morning commute problem with a peak period flat toll on a single bottleneck. Commuters’ values of time are assumed to be continuously distributed. We consider that, for public acceptance reason, the toll has a maximum acceptable toll level and a maximum acceptable length of tolling period, both exogenously given. Under such a constrained optimization setup, we investigate the problems of system cost minimization, Pareto improvement, and revenue maximization. 5 - Research on the Decision Method of Concession Period for Bot Highway Projects Yinhua Yang, Huazhng University of Science and Technology, Luoyu Road 1037,Hongshan District, Wuhan, China, 1143185408@qq.com Private provision of public roads through build–operate-transfer (BOT) contracts is increasing around the world. In this paper, based on a flexible BOT contact with demand updating, We build a decision model of Concession Period for Highway BOT Projects to maximize social welfare and allow the private sector an acceptable profit. Finally, we selects a BOT proiect of Guangfu highway for concession period decision-making analysis. WA72 72-Room 203A, CC Design and Analysis of Computer Experiments Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Ying Hung, Department of Statistics. Rutgers University, yhung@stat.rutgers.edu 1 - Modeling an Augmented Lagrangian for Improved Blackbox Constrained Optimization Robert Gramacy, The University of Chicago, 5807 S Woodlawn Ave, Chicago, IL, 60605, United States of America, rbgramacy@chicagobooth.edu We propose a combination of response surface modeling, expected improvement (EI), and the augmented Lagrangian (AL) numerical optimization framework, allowing the statistical model to think globally and the AL to act locally. Our hybridization presents a simple yet effective solution that allows existing objective-oriented statistical approaches, like those based on Gaussian process surrogates and EI, to be applied to the constrained setting with minor modification. 2 - Empirical Orthogonal Function Calibration with Simulator Uncertainty Matthew Pratola, Assistant Professor, The Ohio State University, 1958 Neil Avenue, Columbus, OH, 43210, United States of America, mpratola@stat.osu.edu Model-assisted inference has become increasingly popular when predicting real- world processes. Computer model calibration is a statistical framework combining mathematical model simulators of the process with statistical techniques to improve predictions by treating the simulator as deterministic. However, these simulators are often uncertain. We develop a Bayesian approach to incorporate such uncertainty using EOF’s, and map simulator uncertainty into inferences for parameters and predictions. 3 - Challenges in Solar Power Forecasting Based on Multiple Computer Models Youngdeok Hwang, Research Staff Member, IBM Research, 1101 Kitchawan Road, Route 134, Rm 34-224, NY, 10603, United States of America, yhwang@us.ibm.com Solar energy forecasting based on large scale computer model needs to address some unique challenges. Development of a forecasting system requires both the accuracy and stability for large scale operation. Statistics has an important role in quantifying uncertainty and providing guidance to decision-makers in the

market. I will discuss some of these unique challenges and research opportunities in using complex computer models in solar energy industry. 4 - Multivariate Gaussian Process Model with Sparse Covariance Estimation Qiong Zhang, Virginia Commonwealth University, 1015 Floyd Avenue, Richmond, VA, 23294, United States of America, qzhang4@vcu.edu We propose a method to obtain a sparse estimation of the covariance matrix for multivariate Gaussian process model. We formulate the optimization problem as the L1 penalized log-likelihood function. To solve the optimization problem, we applied the majorization-minimization approach and the generalized gradient descent algorithm. Numerical experiments are provided to show the computational efficiency of the proposed method. WA73 73-Room 203B, CC Reliability Analysis of Complex Engineering Systems Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Rong Pan, Associate Professor, Arizona State University, P.O. Box 878809, Tempe, AZ, United States of America, rong.pan@asu.edu 1 - Start-up Demonstration Tests with Sparse Connection David Coit, Professor, Rutgers University, coit@rci.rutgers.edu, Xiaoyue Wang, Xian Zhao A start-up demonstration test is important for selecting equipment with high start-up reliability. Based on the concept of sparse connection, three tests with sparse connection are introduced which are called CSTF with sparse d1, TSCF with sparse d2 and CSCF with sparse d3 and d4. By finite Markov chain imbedding approach, probabilistic indexes are given. Besides, the procedure for obtaining optimal parameters of tests is proposed and new tests have higher efficiency than traditional ones. 2 - Bayesian Melding Methods for System Reliability Inference using Multilevel Information Zhaojun Li, Assistant Professor, Western New England University, 1215 Wilbraham Rd, Springfield, MA, 01119, United States of America, zhaojun.li@wne.edu, Jian Guo This paper investigates Bayesian melding methods for the system reliability analysis using various sources of test data and expert knowledge at both subsystem and system levels. The adaptive Sample Importance Resampling (SIR) method is proposed to address the computational challenges for updating the subsystem and system level posterior reliability. System posterior reliability assessments are compared under three scenarios of available subsystem and system data and prior information. 3 - Early System Reliability Analysis for New Products using Existing Components Xiaoping Du, Professor, Missouri Science and Technology University, 400 West 13th Street, Toomey Hall, Rolla, MO, 65409, United States of America, dux@mst.edu A new product may use existing components, and predicting its reliability is difficult due to dependent components. This presentation discusses how to address this issue. One way is to provide a narrow bound of the system reliability by a physics-based approach. The other way is to require component designers provide the relationship between the component reliability and the component load. This allows for an accurate prediction of the system reliability. 4 - A Computational Bayesian Approach to Dependency Assessment in System Reliability Petek Yontay, Research Associate, Arizona State University, 699 S. Mill Ave., Tempe, AZ, 85281, United States of America, pyontay@asu.edu, Rong Pan Due to the increasing complexity of engineered products, it is of great importance to use a model that can handle the complex reliability dependency among components, subsystems and systems. We propose a Bayesian network approach for assessing conditional dependencies within a complex system, using a hierarchical setting. We estimate the posterior distributions of these conditional probabilities in the Bayesian network by combining failure information at component, subsystem and system levels.

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