Graduate Texts in Operations Research – serie

This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems.The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide.

This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models.

It starts with the concept of convex sets, their primal description, constructions, topological properties and dual description, and then moves on to convex functions and the fundamental principles of convex optimization and their use in the complete analysis of convex optimization problems by means of a systematic four-step method.

It starts with the concept of convex sets, their primal description, constructions, topological properties and dual description, and then moves on to convex functions and the fundamental principles of convex optimization and their use in the complete analysis of convex optimization problems by means of a systematic four-step method.

The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods.

The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods.

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.

Embedded in the IBM ILOG CPLEX Optimization Studio with its solver engine CPLEX, OPL has been popular for years not only for academic and scientific purposes, but also among practitioners who need to model and solve large-scale real-world business optimization problems.