CALL FOR PAPERS
Special issue of ITE dedicated to memory of Paul A. Jensen
As theory and practice merge, we have seen dramatic advances in the design and use of analytic methods across all subdisciplines of operations research, industrial engineering and business-related fields. This has been accompanied by an explosive growth in computer power, which has allowed for ever-wider implementation and the solution of very large-scale problems.
On the other hand, much analysis in industry, government and academia occurs with commonly available software, such as spreadsheets, running on personal computers with limited processing speeds. Because of the pervasiveness of spreadsheet software, if we succeed in making our analytical tools available in such software and in making those success stories known, we can reach a wider audience.
For similar reasons, spreadsheet computation can strengthen the teaching of mathematical models and methods, particularly because it can provide a standard interface across topics ranging from optimization and decision analysis to queueing, simulation and statistics.
Paul A. Jensen was a member of the operations research and industrial engineering faculty at the University of Texas for more than 35 years. During that time, he devoted much of his attention to developing computer-based tools to ease the learning curve for those wishing to solve problems arising in operations research, industrial engineering and operations management. His Microsoft Excel add-ins have been employed by hundreds of thousands of users and are freely available at http://www.ormm.net. The breadth and scope of their application is remarkable, with many academic departments adopting them as a foundational tool for their programs.
A special issue of INFORMS Transactions on Education in memory of Professor Jensen will highlight the uses of such tools in teaching, in developing and analyzing case studies, and in practice.
ITE is published electronically, and resources such as case studies, models and data sets can be made available through the Website.
Papers should follow the INFORMS Transactions on Education submission guidelines (see http://www.informs.org/
Pubs/ITE/Submission-Guidelines) and should include a cover letter noting that the paper is for this special issue. Additional information and past issues are available at the journal’s home page (http://ite.
pubs.informs.org).
The editors encourage potential authors to contact them with ideas for papers before a full submission.
Submission timeline:
- Aug. 1 – paper submission deadline
- November – first round of reviews completed; decisions (and requests for revision, if appropriate) delivered to authors
- January 2013 – second round of reviews delivered to authors
- April 2013 – final versions of accepted papers to be submitted
- July 2013 – publication
For further information, contact the co-editors of the special issue: David P. Morton (morton@mail.utexas.edu; 512-471-4104) and Jonathan F. Bard (jbard@mail.utexas.edu; 512-471-3076), Graduate Program in Operations Research & Industrial Engineering, University of Texas at Austin.
Forecasting, data mining and computationally intensive methods
The Operational Research Society (OR Society) of the United Kingdom will hold its annual conference (OR54) at the University of Edinburgh on Sept. 4-6. The conference program contains a mixture of plenary sessions given by keynote speakers and parallel streams covering all aspects of O.R. In addition, the conference features a comprehensive social program and is expected to attract more than 300 delegates.
Fotios Petropoulos (fotis@fsu.gr) of the National Technical University of Athens and John Crocker (john.crocker@o-sys.com) of Optimized Systems and Solutions (OSyS) are organizing a conference stream on “Forecasting, Data Mining and Computationally Intensive Methods,” and they are inviting theoretical and/or empirical contributions that consider this interface.
Forecasting is a multidisciplinary research field drawing on mathematics, management, information sciences, behavioral sciences, social sciences, engineering and other fields. Topics of interest for the OR54 stream include but are not limited to: forecasting principles, forecasting competitions, time series forecasting methods, econometric/causal models, judgmental forecasting, adjusting for special events (promotions, weather), financial and economic forecasting, E-forecasting, energy forecasting, forecasting with neural networks, forecasting support systems and forecasting software evaluation.
During the past decades, data mining has evolved from an academic discipline into a fully-fledged mature technology, with many business applications. This stream addresses the challenges in developing and deploying data mining models in a real-life business setting. Potential topics include but are not limited to: new data mining algorithms and techniques, data preprocessing and data quality, deploying data mining models, monitoring data mining models, real-life case studies, social networks data mining, health and usage monitoring and anomaly detection.
Individuals interested in presenting a paper in this stream should submit an abstract (no more than 300 words) before June 13 at the Society’s Web site (www.theorsociety.com).
