2015 Informs Annual Meeting

TB17

INFORMS Philadelphia – 2015

2 - Computational Study of a Second Order Cone Relaxation for Binary Quadratic Polynomial Problems Julio Goez, Postdoctoral Fellow, Ecole Polytechnique Montreal and GERAD, 2900 Boulevard Edouard-Montpetit, Montréal, QC, H3T 1J4, Canada, jgoez1@gmail.com, Miguel Anjos This work presents a computational study of the second order cone relaxation for binary quadratic problems proposed by Ghaddar, Vera and Anjos (2011) who used a polynomial optimization approach. We explore how this relaxation can be strengthened using additional constraints, and also, we explore the relation of disjunctive conic cuts with this relaxation. 3 - Computational Approaches to Mixed Integer Second Order Cone Optimization Aykut Bulut, PhD Candidate, Lehigh University, 200 W. Packer Ave., Bethlehem, PA, 18015, United States of America, aykut@lehigh.edu, Ted Ralphs We introduce an open-source Mixed Integer Second Order Cone Optimization (MISOCO) solver. We present computational experiments on various approaches to solve MISOCO problem. We test using outer approximation method to solve continuous relaxations. We also discuss using various valid inequalities to improve the continuous relaxations. We discuss computational performance of these approaches on conic benchmark library (CBLIB 2014) problems. 4 - Solving Robust Portfolio Optimization Problems in Practice Sarah Drewes, Senior Consultant, Dr., MathWorks, Adalperostr. 45, Ismaning, Germany, Sarah.Drewes@mathworks.de Robust versions of the Markowitz mean-variance model can reduce the estimation risk induced by its sensitivity to changes in expected returns or the covariance matrix. Probabilistic versions of the classical model can be formulated as nonlinear and often second order cone programs. We study how to solve these problems also by general nonlinear solvers (MATLAB Optimization Toolbox) and in case of discrete variables. We evaluate both computational performance and complexity of implementation.

4 - Network Design Problem for Battery Electric Bus Yousef Maknoon, EPFL, Route Cantonale, Lausanne, Switzerland, yousef.maknoon@epfl.ch, Shadi Sharif Azadeh, Michel Bierlaire In electric bus planning, for battery installation, we need to investigate two points: (1) the type and location of charger stations (2) the capacity of battery of each bus. In this presentation, first we describe the problem and the design elements. Then, we present its mathematical form followed by the resolution approach. Finally, we demonstrate the computational results on our case study and discuss about the robustness of the plan.

TB18 18-Franklin 8, Marriott

Data Mining for Different Type of Big Data Cluster: Modeling and Methodologies in Big Data Invited Session Chair: Young-seon Jeong, Chonnam National University, Department of Industrial Engineering, Gwangju, Korea, Republic of, youngseonjeong@gmail.com 1 - Classification of Uncertain Data using Group to Object Distances Behnam Tavakkol, PhD Candidate, Rutgers University, 5200 BPO Way, Piscataway, NJ, 08854, United States of America, btavakkol66@gmail.com, Myong K (MK) Jeong, Susan Albin Uncertain data problems have features represented by multiple observations or their fitted PDFs. We propose two approaches for classifying uncertain data objects. The first uses existing Probabilistic Distance Measures for object-to-object distances and classifies with KNN. The second features a new probabilistic distance measure for object to class distances. 2 - The Classification Methodology of Chip Quality using Canonical Correlation Analysis Ki-hyun Kim, Samsung Electronics Co., Banwol-dong, Hwaseong-si, Gyeonggi-do, Korea, Republic of, bluenamja@daum.net In this study, we proposed classification methodology using a canonical correlation analysis as feature selection method at multi-dimensional chip level data generated in the semiconductor manufacturing industry. As the result of this research, we were able to extract important varialbes in the varous PCM variables from the correlation of the multiple FBC variables and PCM variables. The proposed method was improved the accuracy of quality classification for a chip tested in the probe test. 3 - Multivariate Monitoring for Metal Fabrication Process in Mobile Devices Manufacturing Seonghyeon Kang, M.s. Candidate, Korea University, Innovation Hall 817, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 136-713, Korea, Republic of, shyeon.kang@gmail.com, Seoung Bum Kim In mobile industry, using metal case of devices is rapidly increased for thin and attractive design. However, fabricating metal is the difficult process because accurate control of equipment are required. In this study, we propose an efficient multivariate monitoring procedure to observe more than 40 parameters of metal fabrication equipment. The effectiveness of the proposed procedure is demonstrated by real data from the mobile plant in one of the leading mobile companies in South Korea. 4 - Multivariate Monitoring of Automated Material Handling Systems in Semiconductor Manufacturing Sangmin Lee, Korea University, Innovation Hall 817, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 136-713, Korea, Republic of, smlee5679@gmail.com, Seoung Bum Kim Monitoring all possible contingencies in automated material handling system (AMHS) of semiconductor manufacturing is a difficult task because tremendous hardware and software systems are involved. This study presents an efficient multivariate monitoring procedure to monitor more than 100 KPIs in AMHS. The effectiveness and applicability of the proposed procedure is demonstrated by real data from semiconductor fabrication plant in one of the leading semiconductor companies in South Korea. 5 - Quantifying the Level of Risk of Functional Chips in Semiconductor Wafers Young-seon Jeong, Chonnam National University, Department of Industrial Engineering, Gwangju, Korea, Republic of, youngseonjeong@gmail.com, Byunghoon Kim, Seung-hoon Tong, Inkap Chang, Myong K (MK) Jeong This talk presents the procedure to quantify the level of risk of functional chips in dynamic random access memory (DRAM) wafers. To screen risky functional chips, the risk level of each chip is estimated by the posterior probability for functional chips. The functional chips closer to the class of defective chips may have a higher probability of being failed in the near future. The experimental results by using real-life wafers show the effectiveness of the proposed method.

TB17 17-Franklin 7, Marriott Network Modeling and Design Sponsor: Optimization/Network Optimization Sponsored Session

Chair: Mario Ventresca, Assistant Professor, School of Industrial Engineering, Purdue University, 315 N Grant St, Lafayette, IN, 47906, United States of America, mventresca@purdue.edu 1 - Action-based Network Models

Viplove Arora, Graduate Student, School of Industrial Engineering, Purdue University, 45 N Salisbury St, Apt. 9, West Lafayette, IN, 47906, United States of America, arora34@purdue.edu, Mario Ventresca

Complex networks are very useful representations of real world complex systems. A number of network generation procedures have been proposed that are capable of producing networks with a restricted subset of structural properties. However, a unifying model remains elusive. We present initial results on an action-based perspective that has potential to yield more general network structures than existing techniques. A machine learning approach to learn a probabilistic model will be presented. 2 - Automatically Inferring Complex Network Models Mario Ventresca, Assistant Professor, School of Industrial Engineering, Purdue University, 315 N Grant St., Lafayette, IN, 47906, United States of America, mventresca@purdue.edu Complex networks are becoming increasingly important across many disciplines. However, the problem of network modeling is extremely intricate and time consuming. Hence, frameworks have been proposed to estimate model parameters, but are focused on capturing a small subset predefined network characteristics such as degree distribution. I will present recent work on a highly robust automated inference approach that is able to discover arbitrary network models with minimal human insight. 3 - Cut-set Separation Schemes for the Robust Single-commodity Network Design Problem Daniel Schmidt, Carnegie Mellon University, 720 S Negley Ave, Pittsburgh, PA, 15232, United States of America, schmidtd@cmu.edu, Chrysanthos Gounaris We address the exact solution of the Robust Single-Commodity Network Design Problem in which customer demands are uncertain and realize from within anappropriately defined uncertainty polytope. We explore techniques to approximate the arc-flow based formulation with fewer variables. We also evaluate the use of meta-heuristics for the NP-hard problem of separating cut-set inequalities in the context of a branch-and-cut solution approach.

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