2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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4 - Real-time Data In Humanitarian Response
Kezban Yagci Sokat, Northwestern University,
, Irina Dolinskaya,
Karen Smilowitz
State of the art humanitarian logistics models have been developed over the past
decades. Most of these models assume availability of data. We study the impact of
granularity in real time data on the humanitarian logistics models. We show that
in the limited data environment higher granularity might lead better results.
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Music Row 1- Omni
Decision Analytics for Technology Management
Sponsored: Technology, Innovation Management &
Entrepreneurship
Sponsored Session
Chair: Tugrul Daim, Professor, Portland State University, Engineering
and Technology Management Department, P.O. Box 751, Portland, OR,
972070751, United States,
1 - GPS For Innovation
Jianxi Luo, Singapore University of Technology & Design,
Engineers, firms or governments continually explore innovation opportunities
and roadmaps. However, related activities and decisions are traditionally based on
intuition or experiences. InnoGPS is developed to provide scientifically-grounded
and data-driven support for decisions regarding innovation directions. It
integrates an empirical network map of technologies that represent the total
technology space, and various map-based functions that allow users to navigate
through the technology space, locate themselves, explore technologies within and
across neighborhoods, and identify capability-building paths. InnoGPS is a “GPS
for Innovation” in the technology space.
2 - Integrating Bibliometrics And Social Network Analysis For
Identifying Knowledge Sources
Tugrul U Daim, Portland State University, ,
Edwin Garces
At an era when technologies are developing rapidly, decision making becomes
even more challenging. However data analytics have shown that data can be used
effectively to help decision making in such environments. Several management
strategies for technological innovations require expert judgments and thus
making the expert identification very crucial. This paper integrates SNA and
Bibliometric Analysis to determine the lead authors and their network. The main
objective of this paper is to present cases from the power sector where this
method was used to identify experts for applications such as technology
roadmapping or forecasting.
3 - Evaluating Research Centers: Case Of NSF’s I/URUC Program
Elizabeth Gibson, Portland State University, 14396 SW Pennywort
Ter, Tigard, OR, 97224, United States,
This research is focused on gaining deeper insights into US National Science
Foundation (NSF) science and engineering research center challenges and
motivated to develop a method that effectively measures the performance of
these organizations. While research has addressed organizational performance at
the micro, or single-actor level for universities or companies and at the regional
or national macro level, the middle level where the NSF centers reside is largely
missing. The bulk of the cooperative research center studies use either case-based
methods or bibliometric data to measure traditional research outputs. Many are
excellent studies; however, they only focus on a piece of the performance
measurement problem. There is a need for more research to understand how to
measure performance and compare performance of cooperative research centers
formed in a triple-helix type partnership involving government, industry and
academia.
4 - Design Support Of Salient Research Project By Integrated
Approach Of Text And Citation Analysis
Yuya Kajikawa, Tokyo Institute of Technology,
Bibliometrics has been a powerful tool to comprehend the current status and to
analyze R&D trends but most of approach is descriptive. We proposed alternative
approach to design salient research project by integrating citation analysis with
text analysis. Explicit research cluster is extracted by citation relationships and
implicit potential ones are by text analysis. This approach can help to find
neglected opportunities between different research domains. This approach can
also visualize plausible path how academic can contribute to development of
industrial technology and to solve social issues. Efficiency and effectiveness of the
approach are demonstrated in case studies.
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Music Row 2- Omni
Analytics and Operations Research for the IT
Services Industry
Sponsored: Service Science
Sponsored Session
Chair: Aly Megahed, IBM Research - Almaden, 650 Harry Road - Office
D3-428, San Jose, CA, 95120, United States,
1 - Maximum Accuracy Is Not Always Optimal
Ray Strong, IBM, San Jose, CA, United States,
Aly Megahed, Janet Blomberg, Pablo Pesce,
Yasuharu Katsuno, Sunhwan Lee
The optimal features of a machine learning classifier or prediction model depend
on the users of the analytics results. When the users have responsibility for acting
on the results, as in the case of a sales force for cloud services, it is often more
important to produce simple, understandable, and credible rules than to optimize
for best prediction accuracy.
2 - An Optimization Approach To Revenue Forecasting In
Multi-Staged Sales Pipelines
Aly Megahed, IBM, San Jose, CA, United States,
, Peifeng Yin,
Hamid Reza Motahari Nezhad
Services organizations manage a pipeline of sales opportunities with variable
engagement lifespans and contract values. Accurate forecasting of contract
signings by the end of a time period (e.g., a quarter) is vital for such organizations
to effectively manage the pipelines. We present a machine learning framework for
this problem and introduce a novel nonlinear optimization approach for finding
the optimized weights of a sales forecasting function. Our model also optimally
determines the number of historical periods to use within the framework. We
present a linear alternative model to the aforementioned model and present
numerical results that show the superior performance of our method.
3 - Value Of Integrated Travel Data To The Organization Productivity
Pawan Chowdhary, Senior Research Engineer, IBM Research,
650 Harry Road, E3-238, San Jose, CA, 95120, United States,
Guangjie Ren, Raphael Arar
In large enterprise, travel is integral part to meet customers, attend events and to
deliver services. But travel data is fragmented from planning a trip to expense
submission due to the sourcing from multiple vendors at each stage. We can
derive greater value by learning from the booking and spend patterns, and
leverage analytics for advanced booking, to negotiate better cost with vendors,
identify market with short term demand forecast, etc. which can bring ten’s of
millions of cost savings. We will present our findings and analysis used to derive
the savings and productivity enhancement.
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Music Row 3- Omni
Modeling, Optimization, and Data Analytic in the
Service Industry
Sponsored: Service Science
Sponsored Session
Chair: Mohammad Sadegh Mobin, Western New England University,
Springfield, MA, United States,
Co-Chair: Zhaojun Li, Western New England University, Springfield,
MA, United States,
1 - Resource Balancing In Intermodal Freight Networks
Amirali Ghahari, University of Arkansas, 4116 Bell Engineering
Center, Fayetteville, AR, 72701, United States, ,
Edward A Pohl
Freight transportation networks provide a system to move containers that are
filled with goods from one point to another. These movements are the main
source of profit for companies. When each node in a network does not have
equal number of incoming and outgoing containers, some nodes will have
surpluses while shortages occur at others. This fact causes accumulation of
containers at a few nodes in the network and shortages at others which would
shut down the transportation network. To resolve this, operators should perform
rebalancing moves. This research examines the planning problem to balance
resources in an intermodal transportation network for one of the major
transportation companies in the US.
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