2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
472
3 - Bank Branch Operational Performance Through a Robust
Multivariate And Fuzzy Clustering Approach
Oscar Albeiro Herrera-Restrepo, PhD. Industrial and Systems
Engineering, Virginia Tech, 4339 Taney Avenue, Apt 401,
Falls Church, VA, 22304, United States, ,
Konstantinos P Triantis, William L. Seaver
We propose a multi-step procedure that integrates fuzzy clustering analysis and
data envelopment analysis (DEA) to group bank branches into managerial
clusters and to investigate their operational performance. We build and expand
on previous research by including fuzzy clustering. We look for changes in
clustering composition due to branches belonging to multiple clusters, and
changes in operational efficiency performance due to fuzzy clustering. All this
while looking at influential branches. Our premise is that fuzzy clustering allows
differentiating clusters beyond scale/size, and that it affects operational efficiency
performance.
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Music Row 2- Omni
Smart Services: Design, Development,
and Measurement
Sponsored: Service Science
Sponsored Session
Chair: Robin Qiu, Penn State, 30 E. Swedesford Road, Malvern, PA,
19355, United States,
Co-Chair: Hi-Hun Kim, Pohang University of Science and Technology,
Pohang, Korea, Democratic People’s Republic of,
1 - Identifying New Service Opportunities For Driving Safety
Enhancement Based On Driving Behavior Analysis: Commercial
Vehicle Case In Korea
Chang-Ho Lee, Pohang University of Science and Technology,
77 Cheongam-Ro. Nam-Gu., Pohang, Korea, Republic of,
, Min Jun Kim, Young-Mok Bae,
Kwang-Jae Kim
The goal of this research is to identify service opportunities for enhancing driving
safety for commercial vehicles (including intra-city buses, express buses, and
trucks). Based on an analysis of vehicle operational data in conjunction with
accident data, new service opportunities for enhancing driving safety are
identified. The service opportunities would contribute to developing new services
for commercial vehicle companies and related authorities in Korea.
2 - Development Of A Daily Health Behavior Index For
College Students
Ki-Hun Kim, Pohang University of Science and Technology,
Pohang, Korea, Republic of, ,
Kwang-Jae Kim
Recently, a smart wellness service has been developed to support daily wellness
management for college students. During a day, the service collects health
behavior (activities, sleep, and diet) data of a student via smart devices. As part of
the service, a daily health behavior index was developed to evaluate the student’s
health behaviors based on the collected data. Daily health behavior data of 47
college students were collected during a four-week experiment and used to
develop the index. This talk presents how the index was developed and utilized in
the service.
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Music Row 3- Omni
E Business/Commerce I
Contributed Session
1 - On-site Personalized Product Recommendations: A Field Study
Dimitrios Tsekouras, Erasmus University, Burgemeester Oudlaan
50, 3062PA, Rotterdam, P.O. Box 1738, Netherlands,
Ting Li
Consumers receive on-site personalized product recommendations about
alternative products based on their past behavior. In this paper, we study the
effectiveness (click and purchase) of these recommendations based on products
(1) browsed, (2) put on wishlist, or (3) bought, using a large dataset from a major
e-retailer. We examine the extent to which these effects differ for products with
different price levels, in different product categories, as well as depending on how
long after the initial interaction with the product source are the recommendations
presented. The findings provide suggestions for improving the on-site
recommendation for e-commerce websites.
2 - The Effect Of Direct Marketing On Online Purchases –
An Empirical Study
Xingyue Zhang, Lehigh University, 621 Taylor Street, Bethlehem,
PA, 18015, United States, , Yuliang Yao
Use a unique dataset collected from one of the largest classified ads website in
China, we empirically examine the effect of offline call intensity on online
customer purchase probability and the carryover effect of call intensity. We find
that online customer purchase probability is increasing in call intensity but at a
decreasing rate. In addition, there exists a strong carryover effect where the call
intensity in the past 4 weeks does not fade away but have a positive effect on
recent customer purchase. Our estimations show that both too much or too little
call intensity will result in considerably worse outcomes.
3 - Product Recommendations In E-mail Marketing: A Randomized
Field Experiment
Ting Li, Erasmus University, T09-14, Burg Oudlaan 50, Rotterdam,
3000DR, Netherlands, Dimitrios Tsekouras
Websites retarget their customers by sending e-mail with personalized product
recommendations based on their past browsing behavior and preferences. In this
study, we examine the effectiveness of such e-mail communication across two
types of product recommendations: content-based recommendations and visual
recommendations. We conducted a large-scale randomized field experiment to
investigate how these effects vary depending on customers’ purchase stage and
the temporal distance between their last website visit and receiving the email. The
study provides insights for e-commerce websites regarding the improvement of
their e-mail retargeting campaigns.
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Music Row 4- Omni
Decision Support Systems II
Contributed Session
Chair: Koki Ho, University of Illinois at Urbana-Champaign, 302F,
Talbot Laboratory, 104 S. Wright St, Urbana, IL, 61801, United States,
1 - A Decision Framework For Uber-like Transportation Platform
Peiyu Luo, PhD Student, University of Louisville, Louisville, KY,
40217, United States, , Lihui Bai
The rising platforms such as Uber, Lyft and Sidecar empower individuals to
provide short-range point-to-point ridesharing services other than traditional
transportation services (e.g. bus, subway, taxi). This paper aims to provide
decision support tools for such peer-to-peer transportation businesses. We divide
the business operations into three stages: resource identification, resource
allocation and task assignment, and use optimization models and prospect theory
in decision making to formulate the three stages. Computational results will be
reported.
2 - Leveraging Machine Learning To Support
Agricultural Decision-making
Emily Burchfield, PhD Candidate, Vanderbilt University, Nashville,
TN, 37212, United States,
John J Nay, Jonathan Gilligan
This project applies machine learning to remotely sensed imagery to train and
validate predictive models of vegetation health. We processed eleven years of
NASA MODIS data and applied gradient boosted machines to the lagged data to
forecast future values of vegetation health. We assessed the predictive power of
our model across space, time, and land use categories. Our models have
significantly more predictive power on held-out datasets than simpler baselines.
We constructed an open source tool that predicts per-pixel vegetation health in a
user-specified region of interest at 16-day intervals. This tool is useful in regions
where clouds prevent real-time monitoring of vegetation dynamics.
3 - Profit Optimization For Rare Earth Permanent Magnet Value
Recovery Under Supply And Demand Uncertainties
Hongyue Jin, PhD Candidate, Purdue University, 2101
Cumberland Ave Apt 2107, West Lafayette, IN, 47906, United
States, Yuehwern Yih, John Sutherland
Rare earth permanent magnets (REPMs) play an essential role in green energy
production and yet face a significant supply risk. To alleviate the risk, value
recovery from end-of-life product is proposed. This research develops an
inventory management strategy for REPM recovery under market supply and
demand uncertainties. A linear programming (LP) model is then developed to
find an upper bound for the proposed strategy. Several scenarios are evaluated
with a hard disk drive (HDD) example. The proposed strategy helps increase the
profit, and the performance is well comparable to the upper bound.
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