2016 INFORMS Annual Meeting Program

WA66

INFORMS Nashville – 2016

WA64 Cumberland 6- Omni Pareto Set Reduction Theories and Methods Sponsored: Multiple Criteria Decision Making Sponsored Session Chair: Daniel Jornada, Texas A&M University, 1700 Research Parkway, College Station, TX, 77843, United States, djornada@tamu.edu 1 - New Notions Of Efficiency For Multicriteria Optimization Under Uncertainty Devon Sigler, PhD Candidate, University of Colorado Denver, Denver, CO, United States, devon.sigler@ucdenver.edu, Alexander Engau We present several new notions of efficiency for multicriteria optimization problems under uncertainty. We demonstrate that these new definitions can be fully characterized theoretically in a hierarchical manner and methodologically using a collection of modified scalarization and generation methods. Related computational comparisons and applications will be discussed. 2 - Robust Solutions To Uncertain Multiobjective Linear Programs Garrett M. Dranichak, Department of Mathematical Sciences, Clemson University, Clemson, SC, United States, gdranic@clemson.edu, Margaret M Wiecek We study highly robust efficient solutions to multiobjective linear programs with uncertainty in the objective function coefficients drawn from finite uncertainty sets. We present results on existence and identification of highly robust efficient solutions, as well as properties of and bounds on the highly robust efficient set. Additional attention is given to a special case of problems yielding a robust counterpart that is easily solvable. 3 - Pareto Set Reduction Theories And Methods Kalyanmoy Deb, Department of Electrical and Computer Engineering, Michigan State University, kdeb@egr.msu.edu In many multi-objective optimization problems, objectives become correlated to each other thereby reducing the dimension of the Pareto-optimal set. In an evolutionary multi-objective optimization method, we have integrated a principal component analysis method to identify redundant objectives and solve very large- scale problems. 4 - A Post-pareto Approach Using A Non-uniform Weight Generator Method With Prioritized Objectives Multi-objective optimization has been recognized as an important research area in the last years since many real life problems present multiple criteria that need to be optimized simultaneously. Using metaheuristic methods or evolutionary algorithms as solution methodologies leads to a large number of Pareto solutions rather than a single unique optimum. Ultimately, all solutions are considered to be Pareto-optimal and selecting the one solution among others can be an arduous task for the decision-maker. This research presents a new developed approach that uses a non-uniform weight generator method to reduce the size of the Pareto-optimal set under the consideration of prioritized objectives. WA65 Mockingbird 1- Omni Digital Transformation of Labor, Media, Telecom, and Financial Markets Sponsored: Information Systems Sponsored Session Chair: Wei Chen, University of Arizona, University of Arizona, Tucson, AZ, 85721, United States, weichen@email.arizona.edu 1 - What Do Employers Look For In Candidates? Xuan Ye, New York University, xye@stern.nyu.edu, Prasanna Tambe Using novel data with descriptions of job interview processes collected from a career intelligence platform, I test the hypothesis that employers assess job candidates’ ability, i.e. problem-solving skills and analytical skills, not labor market experience when they recruit for jobs that require new technical skills. These cognitive skills are hypothesized to be important in a fast moving production environment. Employers’ evaluation methods are measured by text- mining the interview questions contributed by the job candidates. With employer fixed effects estimates, I find that employers use ability-based assessment question 39% more frequently for NewTech jobs than for other jobs. Juan V Fernandez, Industrial, Manufacturing and Systems Engineering Department, University of Texas at El Paso, jvfernandez@miners.utep.edu

2 - New Product Launch With Capacity Constraints And Congestion-sensitive Consumers Duy Dao, University of California, San Diego, Duy.Dao@rady.ucsd.edu, Terrence August, Hyoduk Shin A problem faced by the entertainment industry is the impact of congestion on the release of a product. For theatrical releases, consumers have learned to delay consumption, trading off this congestion cost with the loss of movie buzz as the movie fades in relevance over time. Some may even forgo purchase because of congestion. For online games, a surge of consumers logging on to play an MMORPG can result in server issues during the initial release. In this paper, we model how consumers decide when to make a purchase, considering the congestion they experience. We then offer a strategy for how to profitably expand the market when taking into consideration congestion-sensitive consumers. 3 - The Role Of Technological Discontinuity On Incumbency Advantage Xiahua Wei, University of Washington, Bothell, xhwei@uw.edu This study examines how technological discontinuity contributes to market competition, especially the incumbency of established firms. Based on the theory of barrier to entry, we investigate the consequence of technological change in the mobile telecommunications industry. We find that the ability of new entrants to disrupt incumbents depends on the responsiveness of incumbents to the new technologies. Further, we show that intense competition in the wake of technological discontinuities, driven entirely by incumbents, can delay industry shakeouts. 4 - Taxes And Equity Investment: Evidence From Equity Crowdfunding Wei Chen, University of Arizona, weichen@email.arizona.edu, Mingfeng Lin Given the positive externality that entrepreneurial activities bring to the economy, governments around the world have routinely resorted to various incentives to spur entrepreneurship. In this paper, we empirically study whether and how investments in early stage businesses respond to tax incentives, using a natural experiment due to a policy change, and a comprehensive transaction- level dataset from leading online equity crowdfunding platforms in the United Kingdom. WA66 Mockingbird 2- Omni Data Analytics in Emerging Applications Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Nan Chen, National University of Singapore, Singapore, Singapore, isecn@nus.edu.sg 1 - Inferring Three-dimensional Porous Defects Based On Cross- sectional Images In Metal-based Additive Manufacturing Jianguo Wu, Assistant Professor, University of Texas-El Paso, 500 W University Ave, Engineering Building, A-244, El Paso, TX, 79968, United States, jwu2@utep.edu, Nan Chen, Haijun Gong Porosity is one of the most critical quality issues in the metal-based additive manufacturing. This paper develops a novel quality inspection method by inferring the size distribution, void density and volume fraction of 3D porosity defects based on 2D cross-sectional images. The linkage between the size of ellipsoidal defects and the size of cross-sectional elliptical contours is established. An efficient Quasi-Monte Carlo EM algorithm is developed for 3D size distribution estimation. The relationship between the 3D and 2D void densities is developed to estimate the 3D void density and porosity. The effectiveness of the proposed method is demonstrated through simulation and case studies. 2 - Change-point Detection On Solar Panel Performance Using Thresholded Lasso Youngjun Choe, University of Washington, Seattle, WA, United States, yjchoe@umich.edu, Weihong Guo, Eunshin Byon, Judy Jin, Jingjing Li Solar energy is a fast growing energy source. Solar energy stakeholders are, however, concerned with sudden deterioration of photovoltaic systems’ performance. This study focuses on retrospectively identifying the time points of abrupt changes. We present a nonparametric detection method based on Thresholded LASSO. The proposed method is able to accurately detect performance changes, while being robust against false detection under noisy signals. The performance of the proposed method is evaluated and compared with state-of-the-art methods through extensive simulations and a case study using data collected from four solar energy facilities.

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