© 2018

Practical Python AI Projects

Mathematical Models of Optimization Problems with Google OR-Tools


  • A very practical, hands-on Python book with several projects or case studies to build

  • Come away with real-world templates that you may re-purpose for your own coding projects

  • Written by an industry expert and teacher


Table of contents

  1. Front Matter
    Pages i-xiii
  2. Serge Kruk
    Pages 1-18
  3. Serge Kruk
    Pages 19-61
  4. Serge Kruk
    Pages 63-88
  5. Serge Kruk
    Pages 89-124
  6. Serge Kruk
    Pages 125-160
  7. Serge Kruk
    Pages 161-189
  8. Serge Kruk
    Pages 191-269
  9. Back Matter
    Pages 271-279

About this book


Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,
and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations.

Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study.

You will:
  • Build basic Python-based artificial intelligence (AI) applications 
  • Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suite
  • Create several types of projects using Python and Google OR-Tools


Python AI artificial intelligence math Google optimization tools projects case studies examples code source practical game OR-Tools

Authors and affiliations

  1. 1.MathematicsOakland University MathematicsRochesterUSA

About the authors

Serge Kruk, PhD is a professor at the Department of Mathematics and Statistics at Oakland University and worked for Bell-Northern Research. His current research interests still bear the stamp of practicality enforced by years in industry: algorithms for semidefinite optimization, scheduling, feasibility and the related numerical linear algebra and analysis. After a few wandering years studying physics, computer science, engineering, and philosophy in Montreal in the seventies, the author entered the industrial world and spent more than a decade designing optimization software, telecommunication protocols and real-time controllers. He left Bell-Northern Research, the best geek playground in Canada, to become the oldest student in the Faculty of Mathematics of the University of Waterloo and attach the three letters Ph.D. to his name.The intention, at first, was to return to the real world. But a few years misspent as mathematics and computer science instructor at Waterloo, Wilfrid-Laurier, and finally Oakland convinced him of the appeal of academia. Since then he has wandered as far geographically as Melbourne and as far culturally as l'Ile de la Reunion, mostly teaching and consulting, with the occasional foray into research, guiding a couple of doctoral students through the painful process of dissertation. 

Bibliographic information

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