Optimization Modelling Using R

Timothy R Anderson

ISBN 10: 0367507897 / ISBN 13: 9780367507893

Published by CRC Press Jul 2022, 2022

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers using R for doing optimization, a key area of operations research, which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R.With the rapid rise of interest in data analytics, a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools. Philosophically, the book emphasizes creating formulations before going intoimplementation. Algebraic representation allows for clear understanding and generalizationof large applications, and writing formulations is necessary to explain and convey the modeling decisions made.Appendix A introduces R. Mathematics is used at the level of subscripts and summations Refreshers are provided in Appendix B.This book: - Provides and explains code so examples are relatively clear and self-contained.- Emphasizes creating algebraic formulations before implementing.- Focuses on application rather than algorithmic details.- Embodies the philosophy of reproducible research.- Uses open-source tools to ensure access to powerful optimization tools.- Promotes open-source: all materials are available on the author's github repository.- Demonstrates common debugging practices with a troubleshooting emphasis specific to optimization modeling using R.- Provides code readers can adapt to their own applications.This book can be used for graduate and undergraduate courses for students without a background in optimization and with varying mathematical backgrounds. 298 pp. Englisch.

Seller Inventory # 9780367507893

Bibliographic Details

• Title: Optimization Modelling Using R
• Publisher: CRC Press Jul 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 0367507897
• ISBN 13: 9780367507893
• Binding: Buch
• Condition: Neu
• Dimensions: 237W x 160H x 21D millimeters 237 millimeters width by 160 millimeters height by 21 millimeters depth
• Weight: 564 grams

About this title

Synopsis

This book covers using R for doing optimization, a key area of operations research, which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R.

With the rapid rise of interest in data analytics, a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools.

Philosophically, the book emphasizes creating formulations before going into
implementation. Algebraic representation allows for clear understanding and generalization
of large applications, and writing formulations is necessary to explain and convey the modeling decisions made.

Appendix A introduces R. Mathematics is used at the level of subscripts and summations Refreshers are provided in Appendix B.

This book:

• Provides and explains code so examples are relatively clear and self-contained.
• Emphasizes creating algebraic formulations before implementing.
• Focuses on application rather than algorithmic details.
• Embodies the philosophy of reproducible research.
• Uses open-source tools to ensure access to powerful optimization tools.
• Promotes open-source: all materials are available on the author’s github repository.
• Demonstrates common debugging practices with a troubleshooting emphasis specific to optimization modeling using R.
• Provides code readers can adapt to their own applications
.
This book can be used for graduate and undergraduate courses for students without a background in optimization and with varying mathematical backgrounds.

About the Author

Dr. Timothy Anderson is the Department Chair of the Engineering and Technology Management at Portland State University. He earned an Electrical Engineering degree from the University of Minnesota, as well as both M.S. and Ph.D. degrees in Industrial and Systems Engineering from the Georgia Institute of Technology. He leads the Extreme Technology Analytics research group. With over 40 refereed publications, current research interests include analytics, benchmarking, technology forecasting, data mining, and new product development. He is a Sequoyah Fellow of the American Indian Science and Engineering Society. Dr. Anderson is also a past President of Omega Rho, the International Honor Society for Operations Research.