2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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206A-MCC
Reliability II
Contributed Session
Chair: Mengmeng Zhu, Rutgers University, Piscataway, NJ, 8854,
United States,
1 - Optimal Design Of Hybrid Sequential Testing For A System With
Mixture Of One-shot Units
Elsayed A Elsayed, Distinguished Professor, Rutgers University,
96 Frelinghuysen Rd, Piscataway, NJ, 08854-8018, United States,
Non Destructive Testing is conducted to determine the functionality of the units
without permanent damage in order to estimate the units’ reliability. In this
presentation, we investigate a system composed of non-identical units with
different characteristics and subjected to hybrid reliability testing (Destructive and
NDT). It is of interest to optimally design the hybrid sequential reliability testing.
After conducting a number of hybrid testing, we decrease the sample size of the
destructive testing as the accuracy of reliability metrics estimation improves.
Eventually, we only need to conduct NDT only. The efficiency and accuracy of the
proposed methods are validated.
2 - Transportation Network Fragility And Economic Losses
Narges Kaveshgar, University of South Carolina, 300 Main Street,
Department of Civil and Environmental Engineering, Columbia,
SC, 29208, United States, , Nathan Huynh,
Joseph Von Nessen
Interdependencies between the transportation system and other critical
infrastructures necessitate the need to protect it to achieve system resiliency.
Current study develops a methodology to quantify the robustness and investigate
the reliability of transport network under extreme events. To this end, perishable
field data is collected to determine the impact of the recent road and bridge
closures caused by historic rainfall event in South Carolina to the traveling public
and businesses.
3 - Analyzing Coastal Highway Network Reliability In Hurricane
Flooding Surge Through Geographic Information System
Lei Bu, Institute for Multimodal Transportation, Jackson, MS,
United States, , Feng Wang
Reliability is related to the ability of a network to carry out desire traffic flow
which includes node to node blocking or delay. Based on the history data of
hurricane flooding surge in Gulf coastal region, coastal highway network
reliability is analyzed using a geographic information system. Spatial statistics and
analyst methods based on density, geographic distribution and bilinear
interpolation are used to calculate point density, Z score and hot spots for the
hurricane flooding surge data. Based on the spatial statistics and analyst results,
the blocking or delay links, namely, nodes to nodes, on highway network are
found to determine the network reliability.
4 - Two Phase Imperfect Debugging And Imperfect Fault Removal
Software Reliability Modeling
Mengmeng Zhu, Rutgers University, Piscataway, NJ, 08854,
United States, Hoang Pham
A software reliability modeling considering software fault type and multi-phase
debugging process is proposed in this paper. Type I fault and Type II fault
represent independent and dependent software fault during debugging,
respectively. Two-Phase debugging process are discussed in the model
development. Additionally, a small portion of software faults that software testers
are not able to remove is included in this study due to the limitation of resource
and knowledge.
TA39
207A-MCC
Artificial Intelligence in Big Data
General Session
Chair: Xiao Liu, University of Arizona, 1300 E. Fort Lowell Road G109,
Tucson, AZ, 85719, United States,
1 - Does Interim Winner’s Performance Information Playa Role? An
Empirical Investigation Of The Rank-order Newsvendor Contests
Abraham Seidmann, Simon Business School, University of
Rochester, Simon Business School, Dir of OR Dept, Rochester, NY,
14627, United States,
Tong Wu
Many firms award bonuses to their employees based on their relative
performance. When facing newsvendor-type decisions under this type of inter-
worker competition, firms need to consider what type of information to disclose
to the employees from period to period in order to achieve better outcomes in the
long run. Using a laboratory experiment, we find that publicly displaying the
winner’s performance information every period can significantly improve
individuals’ overall newsvendor decision making compared to the control group,
although the pull-to-center effect is observed. Using another experiment, we find
that impulsivity can explain the observed pull-to-center bias.
2 - Big Data In The Healthcare And Wellness Industry
Stephen J Stoyan, Director, Business Analytics and Strategy,
Abbott Laboratories, 100 Abbort Park Road, Chicago, IL, 60064,
United States,
Today’s healthcare and wellness industry is dynamic, competitive, and global
demands require extremely high volume. Keeping your supply chain lean and
efficient is imperative to driving cost savings. Staying competitive requires a sales
campaign that is connected to customers at new levels. Big data and advanced
analytics are integral parts of the business that provide supply chain efficiencies
and top line growth through strategic and operational initiatives. Analytically
tuned tools are discovering new opportunities, making connections, and creating
business value streams in areas not well understood. We present big data
initiatives at Abbott Laboratories and their impact on the business.
3 - Sales Assistance Search And Purchase Decisions An Analysis
Using Retail Video Data
Aditya Jain, Baruch College, Zicklin School of Business, 55
Lexington Ave, Suite 9-240, New York, NY, 10010, United States,
, Sanjog Misra, Nils Rudi
We investigate the roles of sales assistance and search in driving customer’s
purchase decision using unique observational video data from retail stores. Our
analysis reveals that both sales assistance and search play substantial roles which
differ based on the context of specific decisions—search has a more dominant role
in purchase incidence, whereas the latter in conditional expenditure.
4 - Mining E-cigarette Adverse Events Using The LSTM-based RNN
Model With Word Embeddings Features
Jiaheng Xie, University of Arizona, Department of Management
Information Systems, Tucson, AZ, 6, United States,
The past years have witnessed increased popularity of e-cigarette use across the
world. However, the risk of cartridge fluids and emissions is relatively under-
examined due to limited user sample size. Social media provide a large corpus
that contains e-cigarette related information. In order to study the e-cigarette
adverse effects in a more comprehensive manner, we propose to study the
adverse events of e-cigarette with a large volume of health social media data. The
challenges in e-cigarette safety social media monitoring lie in identifying relevant
adverse events reported by consumers in noisy social media content with high
accuracy. The current automatic entity recognition methods have unsatisfying
performance due to consumer vocabulary used in social media. To address this
issue, we developed a Long Short Term Memory (LSTM) based Recurrent Neural
Network (RNN) model to extract the medical entities. Based on our results, our
proposed LSTM-based RNN model with word embeddings achieved better entity
extraction performance, with a precision of 90.58%, recall of 82.43% and f-score
of 86.31%. We identified 1,212 adverse event entities, 397 e-cigarette component
entities (chemicals, flavors, and brands) and the corresponding component-event
relationships. Since certain e-liquid chemicals, flavors and e-cigarette brands are
significantly associated with adverse events, regulatory actions are in need.
Certain flavors and brands should also be controlled due to their adverse events.
TA40
207B-MCC
Markov Decision Processes: Applications
Sponsored: Applied Probability
Sponsored Session
Chair: Jie Ning, Case Western Reserve University, 11119 Bellflower Rd,
Case Western Reserve University, Cleveland, OH, 44106, United States,
Co-Chair: Matthew J Sobel, Case Western Reserve University - Retired,
11119 Bellflower Rd, Case Western Reserve University, Cleveland, OH,
44106, United States,
1 - Optimal Policies For Risk-averse Electric Vehicle Charging With
Spot Purchases
Daniel Jiang, University of Pittsburgh, Pittsburgh, PA, 15261,
United States, Warren B Powell
We consider the sequential decision problem faced by the manager of an electric
vehicle (EV) charging station, who aims to satisfy the charging demand of the
customer while minimizing cost. We formulate the problem as a finite horizon
Markov decision process (MDP) and provide an analysis of the effect that risk
parameters, e.g., the risk-level used in CVaR, have on the structure of the
optimal policy. We show that becoming more risk-averse in the dynamic risk
measure sense corresponds to the intuitively appealing notion of becoming more
risk-averse in the order thresholds of the optimal policy.
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