Optimisation Models and Methods for Location Planning: With Implementations in Python

The book provides a comprehensive overview of mathematical optimization models and techniques for solving facility location problems. It equips students with the theoretical foundations and practical knowledge needed to analyze real‑world location planning problems, as well as to use and adapt optimization models and methods suited for these tasks. To support this, the book offers detailed explanations of the optimization theory underlying the methods used to solve the presented location problems. In particular, it discusses Lagrangian relaxation and duality, subgradient optimization, column generation, and Benders’ decomposition in substantial depth.

Each chapter concludes with a set of exercises, some of which take the form of case studies that, while fictitious, reflect realistic applications. The book is further supplemented by a Python package, pyloa. At the end of each chapter, examples show how classes and routines from this package can be applied to solve selected location problems introduced in the chapter.

This book is intended for graduate students in operations research, applied mathematics, mathematics‑economics, industrial engineering, and business administration with a focus on analytics or quantitative methods. It is also a valuable resource for university instructors designing 5–15 ECTS courses on location‑planning optimization, as well as for researchers and practitioners seeking a thorough introduction to optimisation models and methods for facility location planning.