Model Building in Mathematical Programming, 4th Edition Review

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(More customer reviews)If there is anything that I would hold against my favorite Operations Research books - it would be the lack of emphasis on model and structure. Williams' book fills in that gap and is an essential companion to every Math Prog book. It is not a cookbook where one can look up a particular problem and the possible ways to model it. Instead, it takes a systematic and very sensible approach to modeling.
The three chapters on Integer Programming Models are amazingly easy to understand and were a real help during a graduate course in the subject. The huge number of practical examples in Parts 2, 3 and 4 of the book is the real value of the book. I would be hard-pressed for space to describe the range of problems that are modeled in Part 2... Part 3 covers a good deal of discussion on these formulations and Part 4 follows it up with solutions. Though solutions are not discussed in detail, they are a great help for someone who has worked hard through the problems and needs a verification of the solutions.
Another useful section in the book is a chapter on the interpretation of Linear Programming solutions. For a person without a Math Prog background (say, a manager), this kind of material is very useful. In fact, it once served as a good refresher for me in a hurry... and an excellent one at that.
The only sore point is a very limited discussion on nonlinear models.

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Review of previous editions'Such a text - and this is the only one of this type I know of - should be the basis of all instruction in Mathematical Programming.' Journal of the Royal Statistical Society'An excellent introduction ... for students of business administration and people who want to see the utility of operations research.' European Journal of Operational Research'It will be appreciated very much by practitioners who already have knowledge in the field of mathematical programming.' Mathematical Programming Society Newsletter Model Building in Mathematical Programming Fourth Edition H. Paul Williams Faculty of Mathematical Studies, University of Southampton, UKThis extensively revised fourth edition of this well-known and much praised book contains a great deal of new material. In particular sections and new problems have been added covering Revenue Management. Hydro Electric Generation, Date Envelopment (efficiency) Analysis, Milk Distribution and Collection and Constraint Programming. The book discusses the general principles of model building in mathematical programming and shows how they can be applied by using simplified but practical problems from widely different contexts. Suggested formulations and solutions are given in the latter part of the book together with computational experience to give the reader a feel for the computation difficulty of solving that particular type of model. Aimed at undergraduates, postgraduates, research students and managers, this book illustrates the scope and limitations of mathematical programming, and shows how it can be applied to real situations. By emphasizing the importance of the building and interpretation of models rather than the solution process, the author attempts to fill a gap left by the many works which concentrate on the algorithmic side of the subject.

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Linear and Nonlinear Programming (International Series in Operations Research & Management Science) Review

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(More customer reviews)I have profitably used the book to apply constrained minimization procedures in the field of computational contact mechanics. I think it is not a secret that quite often books on mathematics are written from matematicians for matematicians. Hence it is quite hard for engineers both to read and to extract valuable information from them. With this respect this book is a shining star. It presents the topics in a very precise but clear and understandable way. Moreover the notation also is the best compromise between coinciseness and clarity. Matematicians, please, look at this book and follow such style; we engineer desperately need to communicate with you.

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AMPL: A Modeling Language for Mathematical Programming Review

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(More customer reviews)Most software "companions" (more than a manual...not quite a book) really do not do justice to the software. Quite the contrary for the AMPL guide. AMPL (the language) is a *very* powerful and *very* easy to use Optimization package. It interfaces with most of the major solvers. Users program in AMPL which is more or less pseudocode and then solve LP, nonlinear, combinatorial, integer, etc. programs. Unlike most software packages, it is both robust and easy to use. Likewise with the companion/book. There are many great, easy to follow examples, and it clearly explains the intrecacies of the language. A must use software and most own book for anyone doing any optimization work.

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AMPL is a language for large-scale optimization and mathematical programming problems in production, distribution, blending, scheduling, and many other applications. Combining familiar algebraic notation and a powerful interactive command environment, AMPL makes it easy to create models, use a wide variety of solvers, and examine solutions. Though flexible and convenient for rapid prototyping and development of models, AMPL also offers the speed and generality needed for repeated large-scale production runs. This book, written by the creators of AMPL, is a complete guide for modelers at all levels of experience. It begins with a tutorial on widely used linear programming models, and presents all of AMPL's features for linear programming with extensive examples. Additional chapters cover network, nonlinear, piecewise-linear, and integer programming; database and spreadsheet interactions; and command scripts. Most chapters include exercises. Download free versions of AMPL and several solvers from www.ampl.com for experimentation, evaluation, and education. The Web site also lists vendors of the commercial version of AMPL and numerous solvers.

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Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets Review

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(More customer reviews)Factors that contributed to a low rating for this book include, a lack of user-friendliness and lengthy case studies. The blue colored wordings (black would be better) can be quite glaring under the lights making it not smoothing to the eyes.
Furthermore, it uses long case studies which could have been shortened by cutting down on the introductions to the companies it made reference to. More focus should be given to concepts at the earlier stage of every section, instead of making the reader running through a lengthy introduction before focusing on the concepts.
Important concepts could also have been left out. One example would be the omission of 'Reduced Cost' under the chapters of Linear Programming and Integer Programming.
However, this book is certainly catered to users of MS Excel. It has in-depth discussions of Excel in areas of Management Science

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These authors are well known for their best selling text, Introduction to Operations Research.This new text is also headed for great success, as it offers a unique case-study approach, and it integrates the use of Excel.Each chapter includes a case study which is meant to show the students a real and interesting application of the topics addressed in that chapter...--This text refers to an out of print or unavailable edition of this title.

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Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence Review

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(More customer reviews)This book is, bar none, the best book on longitudinal analysis in social sciences.
The book has three outstanding features that make it the must-have for researchers who conduct longitudinal studies. First, the book has numerous examples that use data from real studies, collected by prominent scholars in this area. With the help of the accompanying website at UCLA, you will learn how to set up data files, which is crucial in longitudinal analysis. The sample codes and data files in SAS, SPSS, Stata, MLwiN, Mplus, HLM, and Splus will allow you to replicate the analyses. The authors use every effort to explain the results in plain, understandable language. They use a lot of graphs and tables to compare different nested models and help you to choose the one that best describes your data. It feels like you have an excellent tutor by your side when you are reading this book.
Second, the coverage of this book is comprehensive. Part I covers the regular growth curve modeling and multilevel modeling, with a few chapters dealing with time-varying covariates, discontinuous and nonlinear change. Part II covers discrete-time and continuous-time survival analysis. If you are conducting a longitudinal study, chances are you will find a technique in this book that suits you just right.
Third, the book is quite deep. Although it gears toward applications of different longitudinal analyses, it is no cakewalk. You need at least some background in multiple regression and multivariate statistics. I think the treatment of mathematics (both concepts and formulas) is just right. In some sections you may need to revisit them often in order to fully understand the subject.


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Change is constant in everyday life.Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful.Beyond these natural processes and events, external forces and interventions instigate and disrupt change:test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences.It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: Downloadable data sets Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more Additional material for data analysis

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