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Optimization in Operations Research (2nd Edition), by Ronald L. Rardin
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Developing skills and intuitions through accessible optimization models and analysis.
Rardin’s Optimization in Operations Research, Second Edition builds on the critically acclaimed first edition published nearly two decades ago and named Book of the Year in 1999 by the Institute of Industrial Engineers. The goal of the Second Edition is to make the tools of optimization modeling and analysis even more widely accessible to advanced undergraduate and beginning graduate students, as well as to researchers and working practitioners who use it as a reference for self-study. The emphasis lies in developing skills and intuitions that students can apply in real settings or later coursework.
LIke the first, the Second Edition covers the full scope of optimization (mathematical programming), spanning linear, integer, nonlinear, network, and dynamic programming models and algorithms, in both single and multiobjective contexts. New material adds large-scale, stochastic and complexity topics, while broadly deepening mathematical rigor without sacrificing the original’s intuitive style. This edition also continues the author’s belief that making optimization materials accessible and exciting to readers of diverse backgrounds requires a continuing discourse on optimization modeling. Every algorithm and analytic principle is developed in the context of a brief story, and computational exercises often begin with a formulation step.
- Sales Rank: #89564 in Books
- Published on: 2016-01-20
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.60" w x 7.40" l, .0 pounds
- Binding: Hardcover
- 1184 pages
About the Author
Dr. Ronald L. (Ron) Rardin retired as Distinguished Professor Emeritus in 2013 after a 40-year record of leadership as an educator and researcher in optimization methods and their application culminating after 2007 as John and Mary Lib White Distinguished Professor of Industrial Engineering on the faculty of the University of Arkansas-Fayetteville. He headed the University’s Center on Innovation in Healthcare Logistics (CIHL) targeting supply chain and material flow aspects of healthcare operations in collaboration with a variety of healthcare industry organizations. He also took the lead with colleagues at Arkansas in founding the Health Systems Engineering Alliance (HSEA) of industrial engineering academic programs interested in healthcare.
Earlier, Professor Rardin retired in 2006 as Professor Emeritus of Industrial Engineering at Purdue University after completing 24 years there, including directing the Purdue Energy Modeling Research Groups, and playing a leading role in Purdue’s Regenstrief Center for Healthcare Engineering. Previously he had served on the Industrial and Systems Engineering faculty at the Georgia Institute of Technology for 9 years. He also served the profession in a rotation from 2000–2003 as Program Director for Operations Research and Service Enterprise Engineering at the National Science Foundation, including founding the latter program to foster research in service industries.
Dr. Rardin obtained his B.A. and M.P.A. degrees from the University of Kansas, and after working in city government, consulting and distribution for five years, a Ph.D. at Georgia Institute of Technology.
His teaching and research interests center on large-scale optimization modeling and algorithms, especially their application in healthcare and energy. He is an award winning teacher of those topics, and co-author of numerous research papers and two comprehensive textbooks: a graduate text Discrete Optimization, published in 1988, and a comprehensive undergraduate textbook on mathematical programming, Optimization in Operations Research, which was published in 1998 and received the Institute of Industrial Engineers (IIE) Book of the Year award. Among his many other honors, he is a Fellow of both IIE and the Institute for Operations Research and the Management Sciences (INFORMS), as well as 2012 winner of the IIE’s David F. Baker award for career research achievement.
Most helpful customer reviews
27 of 28 people found the following review helpful.
Book Contents
By MAURICIO AGUIAR
The "Search inside this book" feature was not available for this book when this review was published. Hope it helps.
Table of Contents
1. Problem Solving with Mathematical Models
2. Deterministic Optimization Models in Operations Research
3. Improving Search
4. Linear Programming Models
5. Simplex Search for Linear Programming
6. Interior Point Methods for Linear Programming
7. Duality and Sensitivity in Linear Programming
9. Shortest Path and Discrete Dynamic Programming
10. Network Flows
11. Discrete Optimization Models
12. Discrete Optimization Methods
13. Unconstrained Nonlinear Programming
14. Constrained Nonlinear Programming
If you need more information, Professor Rardin (Purdue University) maintains a website that can be easily located using any web search tool.
25 of 28 people found the following review helpful.
Best book after Hillier and Lieberman
By Sarawoot Chittratanawat
I thought there was only one best complete OR book is Hillier and Lieberman (H&L) until I got Rardin's book. Rardin presents the OR in different approach than H&L. The model building part is a little bit better than H&L, but the real meat is the fundamental of optimization. Search technique is well presented from the ground-up. This book can be served as the first book in OR if you're new to OR or the companion book if you know OR or even the first book in optimization in case you don't care to know OR.
24 of 27 people found the following review helpful.
The ultimate introduction to Mathematical Programming
By Sunanda Dutta
I had absolutely no clue about optimization before I read this book. This book not only helped me overcome my diffidence, but also provided me with a very thorough understanding of the basics of the subject.
It is extremely difficult to criticize this book in any way. The language is plain English, not the technical jargon which other "introductory" books use. The book does not assume that the reader has a thorough understanding of linear algebra. Only a course in differential and integral calculus will do. The book provides several primers on a lot of topics- matrices, analysis, vectors etc., so that you do not have to interrupt your optimization studies and digress into these subjects.
There are three full chapters where the author provides real-life examples of mathematical modeling. Again, these are built up step-by-step, and not shoved down your throat.
The different approach to the Simplex Method is more intuitive than the traditional approach and more logical. The basic discussions on integer programming and non-linear programming are also worth mentioning. The numerous exercises and the presence of solutions to a number of them in the end make the text even more helpful.
The only somewhat weak point of the text is the chapter on interior point methods, where the book only provides the formulae and states that the derivations are "beyond the scope of the book". But, this is negligible and I recommend the book wholeheartedly
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