Model Building in Mathematical Programming

by H. Paul Williams, Hilary Paul Williams

Bridges the gap between theory and practice in operational research and management science. The first part discusses general principles of model building in mathematical programming, including discussion of commercially available computer programs. The second part comprises twenty practical problems, and the final sections suggest formulations for, and solutions to, these problems. Throughout, the stress is on building and interpreting the models rather than on the details of the algorithms.

Genres: Mathematics, Science, Textbooks

370 pages, Hardcover

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.