2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016

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210-MCC Social Media and Marketing Analytics Invited: Social Media Analytics Invited Session Chair: Amir Gandomi, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada, agandomi@ryerson.ca 1 - Big Data And Marketing Analytics In Practice: Key Skills And Competencies Amir Gandomi, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada, agandomi@ryerson.ca, Morteza Mashayekhi, Margaret Osborne, Michael Sparling Several recent studies report a significant talent gap in analytics. This study aims to address this gap by establishing a body of knowledge for marketing analytics professionals. We perform a large-scale empirical study using the data from a professional social network. By analyzing the unstructured textual data, we specify the required skills and competencies in different domains of marketing. 2 - Ranking Cuisines And Customers From Geosocial Restaurant Data Syagnik Banerjee, University of Michigan-Flint, syban@umflint.edu We mine location based check-ins and tweets from 40 types of restaurants and 400 visitors across San Francisco, Chicago, Boston, New York, DC and Seattle to fit a 2 parameter IRT model that estimates the cuisine conspicuity of the restaurant and the palate diversity of the visitors. 3 - Learning Product Attribute Embedding Model From Online Reviews Feng Mai, Stevens Institute of Technology, Hoboken, NJ, United States, feng.mai@stevens.edu, Xin (Shane) Wang, David J Curry, Roger Chiang Online reviews provide a valuable source of information to help identify the latest attitudes, opinions, and preferences circulating among consumers. We propose a product attribute embedding model that uses a deep learning approach to learn computable semantic vectors from online reviews. The dimensions of the vector space correspond to latent consumer needs, while product attributes are indexed by their capacity to meet these needs. We demonstrate the implications of the model in market structure analysis and conjoint analysis. 211-MCC Teaching Analytics Using Teradata University Network Resources Sponsored: Education (INFORMED) Sponsored Session Chair: Ramesh Sharda, Oklahoma State University, Stillwater, OK, United States, ramesh.sharda@okstate.edu 1 - Teaching Analytics Using Teradata University Network Resources Ramesh Sharda, Oklahoma State University, ramesh.sharda@okstate.edu Teradata University Network (TUN) is a group of academics supported by Teradata to develop and share teaching and learning resources for analytics. Several thousand faculty and students around the world are using TUN resources. These resources include cases, assignments, software such as SAS Visual Statistics, Teradata Aster, and others. The panelists will describe how they use various TUN resources in their analytics courses. Sharda will introduce TUN and the Aster platform for teaching Big Data technologies. Nestler will describe how he has used sports analytics material. Delen will cover SAS Visual statistics. Bhaskar will discuss how marketing analytics can be taught using TUN resources. 2 - Panelist: Scott Nestler, University of Notre Dame, snestler@nd.edu 3 - Panelist: Dursun Delen, Oklahoma State University, dursun.delen@okstate.edu 4 - Panelist: Rahul Bhaskar, California State University-Fullerton, rbhaskar@fullerton.edu WD49

212-MCC Opt, Nonlinear Programming I Contributed Session

Chair: Harsha Nagarajan, Postdoctoral Research Associate, Los Alamos National Laboratory, 3000 Trinity Drive, Apt 8, Los Alamos, NM, 87544, United States, harsha@lanl.gov 1 - On Broyden-Conjugate Gradient Methods For Solving Unconstrained Optimization Problems Idowu Ademola OSINUGA, Federal University of Agriculture, Abeokuta, Department of Mathematics, Alabata Road, Abeokuta, 234, Nigeria, osinuga08@gmail.com An hybrid methods known as BFGS-CG plays an importatnt role in solving large- scaled optimization problems. Hence, we carried out computational experiments on standard BFGS-CG methods (BFGS-FR, HS & PR). In comparison with the newly introduced hybrid methods BFGS-BAN and BFGS-IMW, the hybrid method BFGS-IMW shows significant improvement in the total number of iterations and CPU time required to solve large-scaled optimization problems. 2 - Fast Solutions With Performance Guarantee For Operational Decisions In Real Time Industrial Gas Network Problems Pelin Cay, Lehigh University, Department of Industrial and Sys Engineering, 200 W Packer Ave, Bethlehem, PA, 18015, United States, pec212@lehigh.edu, Robert H Storer, Luis Zuluaga, Camilo Mancilla In the gas distribution industry, meeting customer demand in real time while meeting the physical constraints in a gas pipeline network leads to complex and challenging optimization problems. We study the performance of different approaches in the literature to either find global optimality or determine the optimality gap between the best local optimum and a valid lower bound. In industry-sized problem instances significant improvements are possible using a better reformulation compared to solving the standard formulation of the problem. 3 - Convex Hulls Of Graphs Of Bilinear Functions On The Unit Cube Fabian Rigterink, University of Newcastle, University Dr, Callaghan, 2308, Australia, fabian.rigterink@uon.edu.au, Natashia Boland, Akshay Gupte, Thomas Kalinowski, Hamish Waterer In his 1989 seminal paper, The boolean quadric polytope: Some characteristics, facets and relatives, Padberg introduced five classes of valid inequalities for the boolean quadric polytope: triangle, clique, cut, generalized cut and odd cycle inequalities. These inequalities outer-approximate the convex hull of a bilinear function. In this talk, we study classes of bilinear functions where some of the Padberg inequalities characterize the convex hull. Furthermore, we study which of the inequalities are strongest, i.e., outer-approximate the convex hull best. We then apply the strong inequalities to (QC)QP instances from the literature to find good lower bounds fast. 4 - A Spatiotemporal Radiotherapy Planning Approach For Cancer Treatment Ali Adibi, PhD Student, Wichita State University, 7413 E 18th Street N, Wichita, KS, 67206, United States, aliadibi.ie@gmail.com, Ehsan Salari Radiotherapy is one of the main modalities for cancer treatment. This research aims at developing a spatiotemporal radiotherapy planning optimization approach and evaluating the potential benefit of varying radiotherapy plans and thus the spatial dose distribution over the treatment course. The proposed approach is applied to a phantom cancer case to test its computational performance. 5 - Tightening McCormick Relaxations For Nonlinear Programs Via Dynamic Multivariate Partitioning Harsha Nagarajan, Postdoctoral Research Associate, Los Alamos National Laboratory, 3000 Trinity Drive, Apt 8, Los Alamos, NM, 87544, United States, harsha@lanl.gov, Mowen Lu, Emre Yamangil, Russell Bent In this work, we propose a two-stage approach to strengthen piecewise McCormick relaxations for mixed-integer nonlinear programs with multi-linear terms. 1st stage exploits constraint programing techniques to contract the variable bounds. In 2nd stage, we partition the variable domains using a dynamic multivariate partitioning scheme via sparse addition of binary variables, which is independent of the size of variable domains. We demonstrate the performance of the proposed algorithm on well-known MINLPLIB problems and discuss the computational benefits of CP-based bound tightening procedures.

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