Applied Mathematics with Open-Source Software Operational Research Problems with Python and R

What it’s about

This book bridges the gap between abstract mathematical theory and practical problem-solving by demonstrating how to tackle a wide range of operational research challenges using free, open-source software. Each chapter introduces a specific problem type—from Markov chains and discrete event simulation to game theory and linear programming—explains the underlying mathematical theory, and then provides parallel, step-by-step implementations in both Python and R. Aimed at students, researchers, and hobbyists, this hands-on guide equips you with the practical skills to model, analyze, and solve complex, real-world problems, making advanced mathematical techniques accessible and immediately applicable.

The through-line

Who it’s for

A student, analyst, or researcher who understands mathematical concepts but struggles to apply them to solve real-world problems using code, or a programmer who wants to understand the mathematical foundations of the problems they are trying to solve. They want to become a competent practitioner capable of tackling complex quantitative challenges.

The problem

Needing to model and solve complex operational or strategic problems (e.g., optimizing schedules, simulating queues, analyzing strategic interactions) but lacking the practical knowledge to implement the appropriate mathematical models in software. Feeling intimidated by the complexity of programming, frustrated by the gap between theory and application, and uncertain about which tools or techniques to use.

The plan

1. Select a problem type from the book's four main sections (e.g., Probabilistic Modelling).
2. Read the chapter's 'Theory' section to understand the core mathematical concepts.
3. Follow the detailed, step-by-step code walkthroughs in your preferred language (Python or R) to solve a concrete example problem.
4. Consult the 'Wider Context' section to explore further research and applications.

The payoff

You can confidently translate real-world problems into mathematical models. · You are proficient in using Python and R to implement simulations, optimizers, and other analytical tools. · You can generate quantitative insights to make better decisions in your work or research.

See our guide

• Run Operations And Supply Chains On Evidence →

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