2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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SB64
Cumberland 6- Omni
Applications and Methodological Issues on MCDM
Sponsored: Multiple Criteria Decision Making
Sponsored Session
Chair: Danielle Morais, Universidade Federal de Pernambuco, Recife -
PE, Brazil,
Co-Chair: Adiel Teixeira De Almeida Filho, Universidade Federal de
Pernambuco, Management Engineering Department, Recife, Brazil,
1 - A Navy Weapon Selection Throughout Fitradeoff
Adiel Teixeira De Almeida Filho, Assistant Professor, Universidade
Federal de Pernambuco, Recife, Brazil, ,
Leonardo A Pessoa, Rodrigo Ferreira, Adiel Teixeira de Almeida
This work presents a multiple criteria decision model for selecting a weapon to be
incorporated in a navy ship using the FITradeoff method. A numerical application
is presented based on realistic data with regard to the real problem faced by a
Brazilian Navy.
2 - A Multicriteria Model For Supplier Selection Based On A
Multilinear Utility Function
Felipe Macedo de Morais Pinto, Universidade Federal de
Pernambuco, ,
Adiel Teixeira De Almeida Filho
This work presents an MCDM model based on a multilinear utility function for
selecting a maintenance service supplier. Depending on the context, maintenance
activities may need to consider other criteria besides cost, which are detailed in
the reference Multicriteria and Multiobjective Models for Risk, Reliability and
Maintenance Decision Analysis.
3 - Computing Interval Weights For Incomplete Pairwise
Comparison Matrices Of Large Dimension – A Weak Consistency
Based Approach
Jana Krejcí, PhD Student, University of Trento, Via Sommarive 9,
Povo, Trento, 38123, Italy,
Jana Krejcí, PhD Student, University of Bayreuth, Universitat sstr.
30, Bayreuth, D-95440, Germany,
Vera Jandova, Jan Stoklasa, Michele Fedrizzi
We present a novel interactive algorithm for large-dimensional pairwise-
comparison problems based on the sequential optimal choice of the pairwise
comparisons (PCs) to be provided by the decision maker and the concept of weak
consistency. The proposed solution significantly reduces the number of needed
PCs by providing sets of feasible values for all missing PCs after each input of a
new PC. Interval weights of objects covering all possible weakly consistent
completions of the incomplete PCMs are then computed from the resulting
incomplete weakly consistent PCM. The algorithm is capable of reducing the
number of PCs required in PC matrices of dimension 15 and greater by more than
60% on average.
SB65
Mockingbird 1- Omni
Learning Analytics of Massive Open Online
Courses (MOOCs)
Sponsored: Information Systems
Sponsored Session
Chair: Sang Pil Han, Arizona State University, Arizona State University,
Tempe, AZ, 85281, United States,
1 - Cohort Size And User Engagement: A MOOC Field Experiment
Jiye Baek, Boston University,
Jesse C Shore
MOOCs have the potential to transform how people access knowledge, but they
face substantial difficulties in keeping users engaged. We conduct a field
experiment on the edX platform to identify factors that promote student
engagement in MOOC discussion forums, focusing on cohort size. While most
prior work show that users in smaller groups participate more per person, our
results show that in the MOOC, the students in larger size cohorts interact more
per person and that this greater interaction in turn increases student retention
and performance. We theorize that larger cohorts produce more forum content
and thus increase the resources available to draw marginal students into an
engaged state.
2 - Towards Improved Education For Students Of Low
Socioeconomic Status: Learning Analytics Of Massive Open
Online Courses (MOOCS)
Sang Pil Han, Arizona State University, Main Campus, PO Box
874606, Office:BA 301D, Tempe, AZ, 85287-4606, United States,
, Mi Hyun Lee, Sunghoon Kim, Sungho Park
Although the new era of free, online learning unfolds, the claim of ‘education for
all’ appears to be overshadowed by the concern over the unequal use of Massive
Open Online Courses (MOOCs). MOOCs may not be a viable solution to students
across all levels of socioeconomic status (SES). Using learner outcome and
demographic data at a MOOC, we examine the effectiveness of two intervention
strategies to improve engagements among low SES learners: (1) course
verification which allows learners to earn an official credit later and (2) mobile
media which enable learners to attend MOOCs anytime/anywhere. From the
findings, we draw implications that can help expand access to education to
everyone through MOOCs.
SB66
Mockingbird 2- Omni
QSR Student Introduction and Interaction and Best
Student Poster Competition
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Nan Chen, National University of Singapore, 21 Lower Kent
Ridge Road, Singapore, 119077, Singapore,
Co-Chair: Kaibo Wang, Tsinghua University, Department of Industrial
Eignieering, Tsinghua University, Beijing, 100084, China,
1 - Student Introduction And Interaction And Best Student
Poster Competition
Nan Chen, National University of Singapore,
This session provides a platform for the interactions between students and senior
QSR members. Participating students will present their research in poster and oral
presentation form. The best poster will be voted and selected among all posters.
We also invite faculty members and industry representatives to interact with
students. They will share valuable experience and provide career advice.
SB67
Mockingbird 3- Omni
Dynamic Data Driven Application Systems
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Chiwoo Park,
Co-Chair: Shiyu Zhou,
1 - Structural Damage Growth Prediction Via Integration Of Finite
Element Method And Bayesian Estimation Approaches
Yuhang Liu, Graduate Student Research Assistant, University of
Wisconsin–Madison, 1513 University Ave., Madison, WI,
United States, , Shiyu Zhou
Damage diagnosis and prognosis play an important role in ensuring the safety of
mechanical and civil structures. Existing works are limited to estimation of the
damage magnitude at the current time instance. Revealing the evolving path of
structural damage is highly desirable for prognosis and remaining useful life
prediction. In this paper, we propose a dynamic data-driven hierarchical Bayesian
degradation model, which takes advantage of both the physical finite element
model and the data driven Bayesian framework, to tackle the structural damage
growth prediction. The damage growth trend can be efficient and accurately
estimated by Gibbs sampling. Numerical and case studies are presented.
2 - Dynamic Data Driven Visual Surveillance Via Cooperative
Unmanned Aerial/ground Vehicles
Sara Minaeian, University of Arizona, Systems and Industrial
Engineering, Tucson, AZ, 85721, United States,
Jian Liu, Young-Jun Son
Unmanned vehicles (UVs) with onboard sensors have recently shown promising
performance in various applications such as autonomous surveillance, compared
to the fixed sensors. However due to the uncertain and dynamically changing
environment, the complex problem of autonomous crowd control requires
robust, multi-scale and effective algorithms to be applied in real-time. In this
work, we propose an autonomous visual surveillance system based on dynamic
data-driven adaptive multi-scale simulation (DDDAMS) for crowd control in a
border area. The experimental results reveal effectiveness of the proposed system
in accomplishing assigned missions under dynamic conditions.
SB67