Several optimization modelling textbooks are available online. Some are digital versions of hard-copy textbooks, while others are solely in web format. Content varies from focussing on theoretical aspects of optimization mathematics, through to practical applications, and developing models in specific programming languages.

This is a book on linear optimization, used for graduate-level courses at the University of Michigan. The examples are written using Python/Gurobi.

Topics include:

- Modeling.
- Algebra versus geometry.
- The simplex algorithm.
- Duality.
- Sensitivity analysis.
- Large-scale linear optimization.
- Integer-linear optimization.

Textbook: A first course in linear optimization.

This book provides a broad introduction to optimization with a focus on practical algorithms for the design of engineering systems. The text is intended for advanced undergraduates and graduate students as well as professionals. The examples are implemented in the Julia programming language.

Topics include:

- Derivatives and gradients.
- Stochastic methods.
- Constrained optimization.
- Sampling plans.
- Optimization under uncertainty.
- Multidisciplinary design optimization.

Textbook: Algorithms for optimization.

This e-book was written as a text for the topic "Decision Modeling" taught as a Business Management Course by David M. Tulett. There are numerous examples implemented using Excel Solver and LINGO.

Topics include:

- Applications of linear models.
- Sensitivity analysis.
- Network models.
- Integer models.
- Goal programming and nonlinear models.
- Decision analysis.

Textbook: Decision modeling.

This is an open textbook in modeling, algorithms, and complexity. The many examples are written in Excel and Python (primarily the PuLP library).

The textbook is a work in progress.

Topics include:

- Linear programming.
- Integer programming.
- Discrete algorithms.
- Nonlinear programming.

Optimization 101 is an online textbook written by Distinguished Research Professor Emeritus John W. Chinneck.

Topics include:

- Linear programming.
- Networks.
- Integer programming.
- Heuristics.
- Dynamic programming.
- Non-linear programming.