2015 Informs Annual Meeting

TB75

INFORMS Philadelphia – 2015

TB75 75-Room 204B, CC IBM Research Best Student Paper Award II Sponsor: Service Science Sponsored Session Chair: Ming-Hui Huang, National Taiwan University, Taiwan - ROC, huangmh@ntu.edu.tw 1 - Best Student Paper Competitive Presentation Ming-Hui Huang, National Taiwan University, Taiwan - ROC, huangmh@ntu.edu.tw Finalists of the IBM Research Best Student Paper Award present their research findings in front of a panel of judges. The judging panel will decide the order of winners, which will be announced during the business meeting of the Service Science Section at the Annual Conference. 2 - Online Network Revenue Management using Thompson Sampling He Wang, MIT, Cambridge, MA, United States of America, wanghe@mit.edu, Kris Johnson Ferreira, David Simchi-Levi Mobile apps have great potential to provide promising services to improve consumers’ engagement and behaviors. Focusing on healthy eating, this study shows that an image-based professional support greatly improves consumer engagement and eating behaviors, while social media and a heuristic approach of self-management might have negative effects in some occasions. Mobile apps have great potential to provide promising services to improve consumers’ engagement and behaviors. Focusing on healthy eating, this study shows that an image-based professional support greatly improves consumer engagement and eating behaviors, while social media and a heuristic approach of self-management might have negative effects in some occasions. 3 - How Environmental Certification Can Affect Performance in the Service Industry: Evidence from the Adoption of LEED Standards in the U.S. Hotel Industry Matthew Walsman,Cornell University, Ithaca, NY United States of America, mcw237@cornell.edu , Suresh Muthulingam, Rohit Verma This study uses a mixed method approach (difference-in-differences and multi- level modeling) to measure the impact of environmental certification (i.e. LEED certification) on financial performance in the US hospitality industry. We find that certification does contribute to higher revenue for the certifying hotel, relative to its competitors. 4 - Optimal Coinsurance Rates for a Heterogeneous Population under Inequality and Resource Constraints Greggory J. Schell, Center for Naval Analyses, 3003 Washington Blvd, Arlington, VA, United States of America, schellg@cna.org, Rodney A. Hayward, Mariel Lavieri, Jeremy B. Sussman We derive prescription coinsurance rates which maximize the health of a hetero- geneous patient population. We analyze the problem as a bilevel optimization model where the lower level is a Markov decision process and the upper level is a resource allocation problem with constraints on expenditures and coinsurance inequality. 5 - Managing Rentals with Usage-Based Loss Vincent Slaugh, Penn State University, Univeristy Park, PA, United States of America, vslaug@cmu.edu, Bahar Biller, Sridhar Tayur We study the operation of a discrete-time stochastic rental system over a single selling season in which rental units may be purchased or damaged by customers. We provide structural results related to the expected profit function and the optimal policy for allocating rental units to meet customer demand. In an industrial use case motivated by a high-fashion dress rental business, we show significant value to accounting for inventory loss and using the optimal inventory recirculation rule.

4 - Wake Effect Characterization in Wind Power Systems Mingdi You, PhD Candidate, University of Michigan, 1205 Beal Avenue, IOE 1773, Ann Arbor, MI, 48109, United States of America, mingdyou@umich.edu, Eunshin Byon, Jionghua (judy) Jin The rapid growth of wind power underscores the need to understand the dynamic characteristics of wind turbine operations. Wind turbines in a wind farm exhibit heterogeneous power generations due to the wake effect. This study provides a computational framework for characterizing the wake effects via a data-driven approach by extending the Gaussian Markov Random field framework. The computational results show that this approach improves the prediction capability over other methods. TB74 74-Room 204A, CC System and Process Informatics in Additive Manufacturing (II) Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Linkan Bian, Assistant Professor, Mississippi State University, 260 McCain Building, Mississippi State, Starkville, MS, 39762, United States of America, bian@ise.msstate.edu 1 - Accelerated Bi-objective Process Optimization for Laser-based Additive Manufacturing (LBAM) Amir M. Aboutaleb, Mississippi State University, 260 McCain Building, Mississippi State, MS, 39762, United States of America, aa1869@msstate.edu, Alaa Elwany, Scott M. Thompson, Linkan Bian, Nima Shamsaei, Mohammad Marufuzzaman Material properties of fabricated parts via LBAM have demonstrated to either be correlated, interdependent or inconsistent with process parameters. In many cases the goal is optimize the LBAM process considering several material properties of interest as a multi-objective problem. We propose a novel methodology for leveraging current experimental data to guide and accelerate the bi-objective Design-of-Experiment process for Pareto Front approximation by the minimum number of experiments. 2 - Spatial Gaussian Process Models for Porosity Prediction in Selective Laser Melting We develop a Gaussian process-based predictive model for predicting the porosity in metallic parts produced using Selective Laser Melting (SLM – a laser-based AM process). A case study is conducted to validate this predictive framework through predicting the porosity of 17-4 PH stainless steel manufacturing on a commercial SLM system. 3 - Automatic Feature Priority Assignment for Automated Production Processes Ola Harryson, Professor, North Carolina State University, 400 Daniels Hall, 111 Lampe Dr, Raleigh, NC, 27606, United States of America, oaharrys@ncsu.edu, Richard Wysk, Sidharth Chaturvedi, Harshad Srinivasan This work describes a system for the prioritization of features at the near-net production stage in order to minimize the effort required for any subsequent finish machining. Heuristics are used to assign weights to features based on value and produceability. A graph of feature relationships is is used to modify the assigned weights based on design and tolerancing principles. An implementation of this system for use with the AIMS hybrid process is described and demonstrated with sample parts. 4 - Additive Manufacturing of Biomedical Implants: Feasibility Assessment via Supply-chain Cost Analysis Adindu Emelogu, Mississippi State University, 260 McCain Building, Mississippi State, MS, 39762, United States of America, emeloguadindu@yahoo.com, Linkan Bian, Mohammad Marufuzzaman We investigate the economic feasibility of fabricating biomedical implants close to hospitals by additive manufacturing (AM) instead of traditional manufacturers (TM) located far from point-of-use. We develop a stochastic mixed-integer programming model which helps to decide the location of AM centers and volume of product flows that minimize supply chain cost. A case study of hospitals in Mississippi, USA recommends AM only when the production cost of AM to TM ratio (ATR) reduces to 3 or less. Alaa Elwany, Texas A&M University, 3131 TAMU, College Station, TX, United States of America, elwany@tamu.edu, Gustavo Tapia, Huiyan Sang

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