Model Building in Mathematical Programming

by H. Paul Williams

This extensively revised and updated edition discusses the general principles of model building in mathematical programming and shows how they can be applied by using twenty simplified, but practical problems from widely different contexts. Suggested formulations and solutions are given in the latter part of the book, together with some computational experience to give the reader some feel for the computational difficulty of solving that particular type of model. The book illustrates the scope and the limitations of mathematical programming and shows how it can be applied to real situations. By emphasizing the importance of building and interpreting 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.

Genres: Mathematics, Science, Textbooks

366 pages, Paperback

First published January 1, 1978

Reviews

[Greg · Rating 4 out of 5]

There is a huge gap between detailed mathematical books on optimization itself, and the technically minded application designer who needs to design good models. This is one of the few books that bridge the gap. I found it to be quite helpful.

[Alice Raffaele · Rating 5 out of 5]

I think this textbook is ideal for university students, as it can be considered as a basic reference text in Operations Research (OR) courses. The first half of the book introduces the main OR topics (such as linear programming and integer linear programming, as well as some hints of nonlinear programming) by referring to numerous other texts, articles, and research papers for further information. It can be seen as the starting point of a student who would like to know and acquire all the basic aspects and skills of modeling, also by trying to get their hands on with some exercises. Indeed, the second half of the book is dedicated to describing 29 problems, analyzing them and providing first the modeling formulations and then the optimal solutions.
Those who already have an OR base knowledge may say it is too introductory. Anyway, it can be of great help in the preparation of university courses or to always have at hand a collection of problems and example models from which to take inspiration to solve their own ones.

[Matthew Mechtly · Rating 5 out of 5]

This book is a practical guide for building LP, IP models (both MIP and PIP models), not for understanding many of the ingenious algorithms that make solving those solutions possible. As a data scientist / engineer with limited OR background, I found this to be a fantastic primer on the subject, and I am now capable of building LP and IP models and conceptually understanding what's happening in the background. Even though Chapters 9 and 10 of Part 1 contained several errors, the errors were still decipherable.