Informs 2015

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INFORMS Philadelphia – 2015
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4 - Optimal Control of an Inventory System with Stochastic and
Independent Leadtimes
Mohsen Elhafsi, Professor, University of California, School of
Business Administration, 900 University Avenue, Riverside, CA,
92521, United States of America, ,
Saif Benjaafar, Rui Chen
We study a continuous review inventory system with stochastic independent
leadtimes. Because orders may not be delivered in the same sequence in which
they have been placed, characterizing the optimal policy is difficult and much of
the available literature assumes a fixed base-stock policy which we show is sub-
optimal and can perform poorly. Instead, the optimal policy is state-dependent
and specified in terms of an inventory-dependent threshold function
characterized by at most m parameters.
TC24
24-Room 401, Marriott
Search Across Disciplines: Artificial Intelligence and
Operations Research
Sponsor: Artificial Intelligence
Sponsored Session
Chair: Nathan Sturtevant, University of Denver, 2280 S. Vine St.,
Denver, CO, 80210, United States of America,
1 - The Cyclic Best-first Search Strategy for
Branch-and-bound Algorithms
Jason Sauppe, University of Illinois at Urbana-Champaign, 201
North Goodwin Avenue, Urbana, IL, 61801, United States of
America, , Edward Sewell, Sheldon Jacobson,
David Morrison
The Cyclic Best-First Search (CBFS) strategy is a generalization of best-first search
that splits unexplored subproblems over a collection of heaps, referred to as
contours. During the search process, CBFS repeatedly cycles through a list of non-
empty contours, selecting one subproblem to explore from each during every
pass. Contours can be defined in various ways to influence the search process.
This talk will present some properties of CBFS and computational results for a
variety of problems.
2 - Adding Random Exploration to Search Algorithms
Rick Valenzano, Alberta Innovates Centre for Machine Learning,
2-21 Athabasca Hall, University of Alberta, Edmonton, AB,
T5K1X4, Canada,
In this work, we will use a simple technique called epsilon-greedy node selection
to demonstrate the value of enhancing search algorithms with random
exploration. Through empirical evaluation, this technique is shown to
substantially improve the performance of search-based automated planners. We
also formally analyze this technique to demonstrate that algorithms that employ
random exploration are more robust to heuristic error.
3 - Exploiting Large Admissible Heuristics in Search
Nathan Sturtevant, University of Denver, 2280 S. Vine St.,
Denver, CO, 80210, United States of America,
Admissible heuristics, which do not overestimate the cost to the goal, are
particularly useful in shortest-path search problems, as they can guide the search.
They also can, if necessary, guarantee optimal solutions. This talk looks at recent
heuristics that are larger than the memory, and suggests ways of exploiting the
problem structure to reduce the memory overhead of storing the heuristic. Two
methods, bloom filters and value range compression are discussed, along with
their tradeoffs.
TC25
25-Room 402, Marriott
Online Crowds: Crowdfunding and Social Media
Sponsor: Information Systems
Sponsored Session
Chair: Qiang Gao, University of Arizona, 3700 N 1st Ave. #1020,
Tucson, AZ, 85719, United States of America,
1 - A Distant Supervision Approach for Social Media
Pharmacovigilance
Xiao Liu, University of Arizona, 1130 E. Helen St.,
Room 430, Tucson, AZ, 85721, United States of America,
Hsinchun Chen
Phamacovigilance refers to the science relating to the detection, assessment,
understanding, and prevention of adverse drug events. Prior studies showed
social media can be used to identify adverse drug events with supervised learning
approaches. However, they require expert annotation and are not scalable for
large datasets. In this study, we develop a framework for pharmacovigilance in
social media using distant supervision. Our framework achieves competent
performance without annotation.
2 - The Effect of Rating System Design on Negativity Bias
Ying Liu, Arizona State University, 1201 S McClintock Dr,
Apt 221, Tempe, AZ, 85281, United States of America,
, Pei-yu Chen, Kevin Hong
Does rating system design affect consumers’ negativity bias in reporting product
ratings? We examine the effect with both observational and experimental study.
Results suggest that consumers tend to reflect their experiences in the least
satisfied dimension in single-dimensional rating systems, whereas the overall
ratings in multi-dimensional systems tend to reflect consumers’ average
experience. The study suggests that multidimensional rating systems could
mitigate consumers’ negativity bias.
3 - Predict Campaign Quality: An Empirical Analysis of the Value of
Video in Crowdfunding Markets
Qiang Gao, University of Arizona, 3700 N 1st Ave. #1020, Tucson,
AZ, 85719, United States of America, ,
Mingfeng Lin
Videos are prevalent in crowdfunding campaigns where there is usually little
verifiable information. Yet to date there is virtually no systematic study of its roles
in this new context. We investigate how video features predict campaign quality
using data from a leading rewards-based crowdfunding website by implementing
both explanatory and predictive models.
4 - Content Monetization in Social Media: Estimation of Demand and
Supply for User Generated Content
Ruibin Geng, Zhejiang University, 388 Yuhangtang Road,
Hangzhou, ZJ, 310058, China,
Bin Zhang, Paulo Goes
Social networking is reaching a maturity stage with fewer new registrations but
more user churning. Our study investigates how a new market mechanism,
content monetization, reduces turnover rate by using data from the largest
Chinese social network Sina Weibo. It examines the factors that affect both the
demand and supply for user-generated content (UGC) in social media. Our results
confirm that this nascent mechanism effectively motivates the supply for UGC
and also improves its quality.
TC26
26-Room 403, Marriott
Gray Market, Sustainability, Competition, and
Diffusion
Cluster: Operations/Marketing Interface
Invited Session
Chair: Samar Mukhopadhyay, Professor, Sungkyunkwan University-
GSB, 25-2 Sungkyunkwan-ro, Jongno gu, Seoul, 110 745, Korea,
Republic of,
1 - Countering Gray Market Threat using Marketing Effort
Samar Mukhopadhyay, Professor, Sungkyunkwan University-
GSB, 25-2 Sungkyunkwan-ro, Jongno gu, Seoul, 110 745, Korea,
Republic of, , Xuemei Su
Gray markets are likely when there is a significant price difference of the same
product in different markets. This paper studies the role of an important variable,
marketing effort, in fighting gray market, in addition to price. We find that when
both marketing effort and prices are controlled, the manufacturer’s profit is
improved. Sometimes, it may even be better not to sell through the authorized
channel, but to manage the gray market by controlling the marketing effort levels
and prices.
2 - Designing Sustainable Products under Co-Production Technology
Yen-Ting Lin, University of San Diego, School of Business
Administration, 5998 Alcala Park, San Diego, CA, United States of
America, Haoying Sun, Shouqiang Wang
We consider a manufacturer who takes a natural resource to make two products
through co-production technology. Some consumers are green and additionally
value conservation of the natural resource. We show that increasing the portion
of green consumers may actually elevate resource consumption.
TC24