This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver. The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the spreadsheet as a solution platform. The emphasis on spreadsheets is another feature that makes this book distinctive. Few books devoted to optimization pay much attention to spreadsheet implementation of optimization principles, and most books that emphasize model building ignore spreadsheets entirely. Thus, someone looking for a spreadsheet-based treatment would otherwise need to use a book that was designed for some other purpose, like a survey of management science topics, rather than one devoted to optimization. The model building emphasis derives from an attempt to be realistic about what readers need most when learning about optimization.

At an introductory level, the most practical and motivating theme is the wide applicability of optimization tools. To apply optimization effectively, readers needs more than a brief exposure to a series of numerical examples, which is the way that most mathematical programming books treat applications. With a systematic modeling emphasis, readers can begin to see the basic structures that appear in optimization models and as a result, develop an appreciation for potential applications well beyond the examples in the book. Formulating optimization models is both an art and a science, and this book pays attention to both. The art can be refined with practice, especially supervised practice, just the way a student would learn sculpture or painting. The science is reflected in the structure that organizes the topics in this book. For example, there are several distinct problem types that lend themselves to linear programming formulations, and it makes sense to study these types systematically. In that spirit, the book builds a library of templates against which new problems can be compared.

Analogous structures are developed for the presentation of other topics as well.