2016 INFORMS Annual Meeting Program

WE77

INFORMS Nashville – 2016

WE78 Legends F- Omni Opt, Linear Programing Contributed Session

2 - Resource Optimization Based On Data Envelopment Analysis - Dynamic Programming Method Tao Du, Beijing Insititute and Technology, Beijing, China, dutao0608@163.com As the resource optimization is one of main approach to improve organization’s efficiency, we propose a DEA-Dynamic Programming (DEA-DP) method which is a resource optimization method combining with the DEA model for measuring efficiency and the Dynamic Programming method for multistage decision. The basic ideas of the method is to determine the optimal resource planning strategy for improving the inter DMUs’ relative efficiency of organization through Dynamic Programming method. Through a case study, we proves investment can be saved by 11.23% using the method.

Chair: Joseph L Trask, North Carolina State University, 3500 Mill Tree Rd, Apt B2, Raleigh, NC, 27612, United States, jltrask@ncsu.edu 1 - Optimal Assignment Of Inspection Stations In A Flowline Md Shahriar Jahan Hossain, Louisiana State University, 2508 Patrick F. Taylor Hall, Baton Rouge, LA, 70803, United States, msjhossain1@gmail.com, Bhaba R Sarker The research deals with multiple in-line inspection stations that partition a production flowline into multiple flexible lines. A unit cost function is developed for determining the number and locations of in-line inspection stations along with the alternative decisions on each type of defects: scrapping or reworking (on/off-line). The problem is formulated as fractional mixed-integer nonlinear programming problem to minimize the unit cost of production, and solved with branch and bound method. A construction heuristic is also developed to determine a sub-optimal solution for large instances. 2 - The Double Pivot Simplex Method Fabio T Vitor, Graduate Teaching Assistant, Kansas State University, 2061 Rathbone Hall, 1701B Platt St., Manhattan, KS, 66506, United States, fabioftv@k-state.edu, Todd W Easton The Simplex Method (SM) is considered one of the top 10 algorithms of the 20th century. Each iteration of SM pivots between basic feasible solutions by exchanging one nonbasic variable with a basic variable. This talk presents the Double Pivot Simplex Method (DPSM), which can pivot on two variables. The Slope Algorithm is a new method that replaces the ratio test of SM, and guarantees the optimal basis at every iteration of DPSM. Furthermore, each iteration of DPSM not only has the same theoretical running time as SM, but also improves the objective value by at least as much as an iteration of SM. Computational experiments demonstrate that DPSM is approximately 15% to 40% faster than SM. 3 - Inverse Optimization For Utility Measurement Yu-Ching Lee, National Tsing Hua University, Hsinchu, Taiwan, ylee77@illinois.edu, Yi-Hao Huang, Ciou Si-Jheng Utility function has been prevalent to express one consumer’s preference representing consumer’s demand. We formulate a mathematical program with quadratic objective function and complementarity constraints as the inverse problem that minimizes the error of the measured utility function. Our research indicates that the program with complementary constraints will help us find a set of more accurate parameters. 4 - Determining The Aggregate Plan: A Cross-functional Perspective Kathleen Iacocca, Villanova University, Villanova, PA, United States, kathleen.iacocca@villanova.edu, Kingsley Gnanendran The traditional aggregate plan is extended to include marketing and financial aspects. On the marketing side, we determine the optimal price and demand for each period, while on the financial side we include month-by-month collections, taxes, interest on loans and/or return on surplus funds, depreciation, and minimum cash balances in determining optimal production levels. The problem is modeled as a linear program and implemented on a spreadsheet to demonstrate ease of managerial applicability.

WE77 Legends E- Omni Opt, Large Scale III Contributed Session

Chair: Tao Jiang, Purdue University, 403 W. State Street, Krannert School of Management, West Lafayette, IN, 47906, United States, taujiang@purdue.edu 1 - Robust Principal Component Analysis In Multiclass Problem Structures Sam Davanloo, Ohio State University, Columbus, OH, United States, sdt144@vt.edu, Xinwei Deng Robust Principal Component Analysis (RPCA) is mainly used to decompose a data matrix to a low rank and a sparse component, and has applications in face recognition, video surveillance, latent semantic indexing, and etc. In this study, we consider a multiclass structure in which classes share a common low rank component, a class-specified low rank component, and a class-specific sparse component. RPCA is then utilized to estimate these components. A first-order optimization method is proposed to solve the problem in high-dimensional settings. Numerical simulation results support the proposed methodology. 2 - Stability Of The Stochastic Gradient Method For Approximated Large Scale Kernel Machine Using Random Fourier Features Aven Samareh, PhD Student, University of Washington, 4324 8th ave NE, D7, Seattle, WA, 98105, United States, asamareh@uw.edu, Mahshid Salemi Parizi We measured the stability of stochastic gradient method (SGM) for learning an approximated Fourier support vector machine. The stability of an algorithm is considered by measuring the generalization error in terms of the absolute difference between the test and the training error. Our problem is to learn an approximated kernel function using random Fourier features for binary classification data sets via online convex optimization settings. We showed that with a high probability SGM generalize well for an approximated kernel under a convex, Lipschitz continuous and smooth loss function given reasonable number of iterations. We empirically verified the theoretical findings as well. 3 - Modeling Improvements And Refinements To The Fleet Modernization Capability Portfolio Analysis Tool Frank Muldoon, Sandia National Labs, 1525 Summit Hills Drive NE, Albuquerque, NM, 87112, United States, fmmuldo@sandia.gov, Matthew Hoffman, Stephen Henry, Lucas Waddell, Peter Backlund The Capability Portfolio Analysis Tool (2015 Edelman Finalist) is currently being used to model both the fleet of ground combat systems under the U.S. Army PEO Ground Combat Systems and the fleet of logistics and support systems under PEO Combat Support & Combat Service Support to provide analytical capability in support of modernization and investment decisions. This large-scale multi-phase MILP has evolved over the last year to meet the challenges posed by both PEOs including the incorporation of budgetary earmarks, system age, and modeling techniques developed to mitigate its large size. 4 - An Iterative Rounding Algorithm And Almost Feasibility For Nonconvex Optimization Tao Jiang, Purdue University, 403 W. State Street, Krannert School We consider a class of high-dimensional non-convex minimization problems for which the objective is separable and constraints are linear. We solve a convex relaxation to obtain a lower bound and to restrict the original problem to an integer program. Then, we study the trade-off in approximation gap between admitting solutions that violate some constraints slightly versus not allowing such solutions. In particular, we show that if we admit solutions that “almost” satisfy the constraints with small coefficients, the iterative rounding procedure can find a solution much closer to the relaxation bound than if we disallow such solutions. We discuss applications of this result in various settings. of Management, West Lafayette, IN, 47906, United States, taujiang@purdue.edu, Thanh Nguyen, Mohit Tawarmalani

WE80 Broadway E- Omni Retail Mgt II Contributed Session

Chair: H. Sebastian Heese, EBS University, ISCM, Burgstr. 5, Oestrich-Winkel, 65375, Germany, sebastian.heese@ebs.edu 1 - Consumer Perceptions Of Return Policies Yue Cheng, Pennslyvania State University, 460A Business

Building, University Park, PA, 16802, United States, yuc190@psu.edu, Daniel Guide, Margaret Meloy

This study investigates consumer perceptions of three types of return policies by using survey data. The consumers responded to the amount of discounts and premiums associated with different return policies and brand equity. We provide managerial insights for firms to take advantages of using different return policies strategically.

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