2015 Informs Annual Meeting

TA12

INFORMS Philadelphia – 2015

4 - Evaluating Technology Adoption in Emerging Regions: Case of Smart Phone in Saudi Arabia Fahad Aldhaban, Portland State University, P.O. Box 751, Portland, United States of America, aldhaban@gmail.com, Tugrul Daim This paper reviews the adoption factors of smart phones in emerging regions. Saudi Arabia is studied as a case study. This presentation will cover the qualitative part of the work. This part helped filter factors and finalize the survey instrument TA10 10-Room 310, Marriott Contextual Factors Affecting eBusiness Initiatives Chair: Frank MacCrory, Massachusetts Institute of Technology, MIT Initiative on the Digital Economy, 355 Main Street - NE25-768D, Cambridge, MA, 02142, United States of America, maccrory@mit.edu 1 - Social Media Usage Implications for Project Success, Political Preferences, and Leisure Activities Joseph Vithayathil, Assistant Professor, Washington State University, Carson College of Business, Pullman, WA, 99164, United States of America, joseph.vithayathil@wsu.edu, John Kalu Osiri, Majid Dadgar We use a survey to empirically analyze the effect of social media usage on workplace project success, political preferences, and leisure activities such as shopping and television viewing behavior. This work adds to the emerging literature on the impact of social media. We find weak association of social media usage with project success, political preferences and leisure activities. Results are interpreted using social presence and media richness theories, and implications are discussed. 2 - Content Pricing Strategies under Dual Medium Access Ran Zhang, UC Irvine, CA, ranz2@uci.edu, Shivendu Shivendu Pricing information goods on physical and digital medium is a challenging question for content providers. We develop an analytical model where consumers are heterogeneous in both valuation for content and preference for medium. We show that while offering both bundle of mediums and digital medium is optimal under some market conditions, offering digital medium only is optimal under other conditions. The optimal price for digital medium can decrease with marginal cost of physical medium. 3 - Incentives for Selective Information Sharing Aditya Saharia, Associate Professor, Gabelli School of Business - Fordham University, 113 W. 60th Sreet, New York, NY, 10023, United States of America, saharia@fordham.edu An increased transparency in inter-organizational systems does not make members of a value chain equally better off. Individual members may then try to influence other members’ decisions by introducing strategic ambiguity by not collect demand information or by selectively share information with only some downstream members. Online Optimization with Integer Applications Sponsor: Optimization/Integer and Discrete Optimization Sponsored Session Chair: Virgile Galle, PhD Candidate, MIT, vgalle@mit.edu 1 - Real-time Revenue Management under Partially Learnable Demand Dawsen Hwang, PhD Candidate, MIT, 77 Massachusetts Avenue, 32-D678, Cambridge, MA, 02139, United States of America, dawsen@mit.edu, Le Nguyen Hoang, Vahideh Manshadi, Patrick Jaillet We study a real-time revenue management problem where stochastic information about the future demand is unknown a priori and can only be partially learned. We develop adaptive and non-adaptive booking-limit policies parameterized by predictability of the demand. In the two extreme cases of fully learnable and fully unpredictable demand, we recover the known performance guarantees. Our work bridges the gap between classical adversarial and stochastic demand models, and defines value of learning. Sponsor: E-Business Sponsored Session TA11 11-Franklin 1, Marriott

2 - Container Relocation Problem with Partial Information Virgile Galle, PhD Candidate, MIT, Cambridge, MA, vgalle@mit.edu, Cynthia Barnhart, Setareh Borjian, Patrick Jaillet, Vahideh Manshadi

We introduce two new versions of the container relocation problem. First we suppose that container departure times are only partially known and propose an efficient branching algorithm using sampling and pruning to solve this problem. Moreover, the second variation assumes that none of the departure times are known in advance. In that case, we provide lower bounds to support the intuition that the “lowest-height” policy is optimal in both static and dynamic case 3 - Taxi Assignment: Offline and Data-driven Online Optimization Sebastien Martin, PhD Candidate, MIT, Operations Research Center, MIT, 77 Mass Ave, Bldg E40-130, Cambridge, MA, United States of America, semartin@mit.edu, Dimitris Bertsimas, Patrick Jaillet This research focuses on taxi routing and assignment to customers: we optimize the actions and revenues of a taxi fleet. We use MILPs and randomized algorithms to solve to optimality the full-information version of the problem where demand is known beforehand. Then, we extend these methods to make data-driven online decisions. We apply our methods on the Manhattan network using actual 2013 yellow cabs demand data. 4 - Online Packing in the Random Time Arrival Model Le Nguyen Hoang, Postdoctoral Associate, MIT, 77 Massachusetts Much interest has recently been given to online packing under a uniformly random permutation of request arrivals. We propose a more general and realistic setting, where, instead, requests arrive at random times. In particular, we do not assume the number of requests to be known ahead of time, and we allow for heterogeneity in the probability distributions of the random arrival times. We present different online algorithms and discuss their respective competitive ratios. TA12 12-Franklin 2, Marriott Convexification-based Algorithms for Solving Quadratic and Polynomial Programs Sponsor: Optimization/Mixed Integer Nonlinear Optimization and Chair: Jitamitra Desai, Professor, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore, jdesai@ntu.edu.sg 1 - Minimum Triangle Inequalities and Algorithms for 0-1 QCQPs Jitamitra Desai, Professor, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore, jdesai@ntu.edu.sg, Xiaofei Qi, Rupaj Nayak We present a new class of minimum triangle inequalities (MINTI) for 0-1 QCQPs. We prove that these inequalities are superior to the traditionally used triangle inequalities, and offer several variations of these new cutting planes. We also present an improved branch-and-bound algorithm that incorporates certain properties from the MINTI cuts, and prove the efficacy of these cuts via our computational results. 2 - Non-negative Polynomial and Moment Conic Optimization Mohammad Mehdi Ranjbar, Rutgers, 100 Rockafeller Rd, Rutgers Business School, Piscataway, NJ, 08854, United States of America, 59ranjbar@gmail.com, Farid Alizadeh Non-negative polynomial cone and its dual, moment cone, are non-symmetric cones and extremely bad scaled. Then common primal-dual method will not be a good algorithm to be used. Recently Nesterov has proposed a new predictor- corrector path-following method. Skajaa-Ye have proposed a Homogeneous interior point method using Nesterov’s predictor-corrector path-following method for some non-symmetric conic problem. We will extend that to non-negative polynomial and moment conic programming. 3 - Robust Sensitivity Analysis of the Optimal Value of Linear Programming Guanglin Xu, PhD Student, University of Iowa, 321 Finkbine Ln Apt. 11, Iowa City, IA, United States of America, guanglin- xu@uiowa.edu, Samuel Burer We study sensitivity analysis in linear programming problems where general perturbations in the objective coefficients and right-hand sides are considered. This generality leads to non-convex quadratic programs (QPs) that are difficult to solve in general. We investigate copositive formulations and tight semi-definite relaxations of these QPs and validate our approach on examples existing in the literature, as well as our own examples. Ave, Cambridge, MA, 02139, United States of America, lenhoang@mit.edu, Dawsen Hwang, Patrick Jaillet Global Optimization Sponsored Session

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