2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - A Polyhedral Study Of The Integrated Minimum-up/-down Time
And Ramping Polytope
Yongpei Guan, University of Florida, Gainesville, FL, United States,
Kai Pan
We study the polyhedral structure of an integrated minimum-up/-down time
and ramping polytope, which has broad applications in variant industries. By
exploring its structures, we derive strong valid inequalities and explore a new
proof technique to prove these inequalities are sufficient to describe variant two-
period and three-period convex hulls. For multi-period cases, we derive
generalized facet-defining strong valid inequalities with efficient polynomial time
separation algorithms to improve the computational efficiency. Extensive
computational experiments are conducted to verify the effectiveness of our strong
valid inequalities.
4 - Integrated Expansion Planning Framework For Interconnected
Power Systems; Heat Supply; And Gas Infrastructure
Yasaman Mozafari, University of Calgary, Calgary, AB, Canada,
, William Rosehart
Increasing gas-fired generation capacity and interest in highly efficient combined
heat and power generation units (CHPs) in power systems imply
interdependencies between electricity, heat, and gas infrastructure. More efficient
expansion planning results can be obtained by effectively modeling these
couplings in the planning optimization problem. In this work, a comprehensive
integrated framework for expansion planning of power systems, heat supply, and
gas infrastructure is proposed. Modeling the independencies substantially reduces
the cost and GHG emissions incurred in energy sector, which is illustrated
through the simulation results for Alberta energy system.
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101E-MCC
2016 INFORMS BOM Section Best Working
Paper Awards
Sponsored: Behavioral Operations Management
Sponsored Session
Chair: Stephen Leider, University of Michigan, Ann Arbor, MI,
1 - A Behavioral Study On Abandonment Decisions In
Multi-Stage Projects
Javad Nasiry, Hong Kong University of Science and Technology,
Clear Water Bay, Kowloon, Hong Kong, ,
Xiaoyang Long, Yaozhong Wu, Yaozhong Wu
We experimentally investigate continuation/abandonment decisions in a multi-
stage project under two conditions: when the project is reviewed at every stage
and when review opportunities are limited. We find systematic deviations from
the optimal solution: subjects may wrongly continue or abandon the project, and
their decisions are path dependent. We propose a behavioral model which
explains the behavioral regularities.
2 - Ideation-Execution Transition In Product Development
Evgeny Kagan, University of Michigan, Ann Arbor, MI,
Stephen Leider, William Lovejoy
We show experimentally that design performance is significantly worse when
designers decide for themselves how to schedule the development process. We
demonstrate several remedies for situations when external allocation of time to
development phases is not possible. Managers can improve performance by
“nudging” individuals towards early physical build, or by requiring them to com-
mit to a transition time beforehand. However, the most effective way to improve
performance is contingent transition - a requirement to present a prototype that
exceeds a minimum performance hurdle.
3 - Impact of Queue Configuration On Service Time:
Evidence From A Supermarket
Yong-Pin Zhou, University of Washington, Seattle, WA,
, Jingqi Wang
We study how queue configuration affects server’s service time using data from a
supermarket. We find that servers in dedicated queues are about 10.7% faster
than those in pooled queues, after controlling for the queue length, mainly due
to a direct social loafing effect. We also demonstrate that pooling has an indirect
negative effect on the service time through its impact on the queue length. In
aggregation, the social loafing effect dominates and servers slow down (a 6.86%
increase in service time) in pooled queues.
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102A-MCC
Data Mining in Text Analytics
Sponsored: Data Mining
Sponsored Session
Chair: Onur Seref, Virginia Tech, Pamplin 1007, Blacksburg, VA, 24061,
United States,
1 - Tracking The Evolution Of User Interests In Online Communities
Theodoros Lappas, Stevens University of Technology,
Online communities are the hubs of our virtual world. A community is typically
focused on a broad area, such as sports or politics. Interested users participate in
the community by joining discussion threads on relevant topics within the scope
of the general theme. In this work, we hypothesize that a user’s level of interest
in each topic is correlated with her maturity within the community. We evaluate
our hypothesis on datasets from different domains and present a temporal user-
interest model. Our study provides insight on the nature of user generated
content and has strong implications for any application based on user interests.
2 - A Network-based Model For Conversation Decomposition
In Text Mining
Sukhwa Hong, Virginia Tech, Blacksburg VA, United States,
, Onur Seref, Michelle Seref, Alan Abrahams
We present a network-based framework to identify and cluster conversational
phrases in classes of text data using prevalence scores of n-gram structures and
their connections. Our framework extends the “bag-of-words” models by
network-based clustering methods to create sub-graphs of connected n-grams.
The paths in these sub-graphs represent sequences of words, which form
conversational phrases with richer contextual meaning. We use sequence
alignment methods to identify variations of these phrases and apply the proposed
framework to study a collection of discussion posts from the automotive industry.
We compare effectiveness of our method to standard “bag-of-words” models such
as LDA.
3 - Two Are Better Than One? An Empirical Study On Crowd
Performance For Stock Prediction
Hong Hong, Xiamen University, Xiamen, China,
Qianzhou Du, Alan G. Wang, Weiguo Fan, Di Xu
Online investment communities have been a popular venue for individual
investors to share and interact with each other. Prior research confirms the
importance of crowd wisdom in the stock markets context, but fails to investigate
the impact of crowd characteristics on crowd performance. Guided by the Crowd
Wisdom theory, we conduct an empirical study using data collected from
Stocktwits to fill this research gap. Our findings show that diversity,
independence, and decentralization are positively related to crowd performance.
In addition, crowd size significantly moderates the influence of crowd
characteristics on crowd performance. This study has both theoretical and
practical implications.
4 - An Intelligent Multilayer Hotel Recommender System
Ashkan Ebadi, University of Florida, Gainesville, FL, United States,
Adam Krzyzak
Techniques behind the recommender systems have been improved over the time.
Recommenders help users to find their required products or services through
analysing and aggregating other users’ activities and behaviour. In this paper, we
propose an accurate multi-layer hybrid hotel recommender system that uses
multi-aspect rating. We used large-scale data of different types and designed a
system that is able to suggest hotels which are tailored to the given user. The
system employs natural language processing and topic modelling techniques to
assess sentiment of users’ reviews. The recommender engine contains several sub-
systems where each sub-system contributes to the final recommendations.
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