Informs 2015

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INFORMS Philadelphia – 2015
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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, ,
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
Sponsor: E-Business
Sponsored Session
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,
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, ,
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, , 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,
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.
TA11
11-Franklin 1, Marriott
Online Optimization with Integer Applications
Sponsor: Optimization/Integer and Discrete Optimization
Sponsored Session
Chair: Virgile Galle, PhD Candidate, MIT,
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,
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.
2 - Container Relocation Problem with Partial Information
Virgile Galle, PhD Candidate, MIT, Cambridge, MA,
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, , 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
Ave, Cambridge, MA, 02139, United States of America,
Dawsen Hwang, Patrick Jaillet
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
Global Optimization
Sponsored Session
Chair: Jitamitra Desai, Professor, Nanyang Technological University,
50 Nanyang Avenue, Singapore, Singapore,
1 - Minimum Triangle Inequalities and Algorithms for 0-1 QCQPs
Jitamitra Desai, Professor, Nanyang Technological University, 50
Nanyang Avenue, Singapore, Singapore,
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, 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-
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.
TA12