The MILP Optimization Handbook: A Comprehensive Guide to Mixed-Integer Linear Programming, Algorithms, and Real-World Applications - Tapa blanda

Sinopsis

Unlock the power of Mixed-Integer Linear Programming (MILP) to solve real-world optimization problems.

The MILP Optimization Handbook is a complete guide for graduate students, researchers, engineers, and data scientists who want to master MILP from theory to practice. Written in a clear and accessible style, this handbook blends rigorous mathematics with hands-on examples, making it both a reference guide and a practical tutorial.

Inside you will discover:

• Foundations of MILP: Core concepts, mathematical formulations, and polyhedral theory explained step by step.

• Algorithms and Solvers: Deep insights into branch-and-bound, cutting planes, decomposition, and modern solver technologies (Gurobi, CPLEX, OR-Tools, CBC).

• Advanced Techniques: Preprocessing, relaxations, decomposition, and robust optimization strategies.

• Practical Applications: Real-world case studies in supply chain, energy systems, finance, scheduling, telecommunications, and machine learning.

• Hands-On Modeling: Complete Python examples with Pyomo, PuLP, Gurobi, and CPLEX, plus solver tuning tips for better performance.

• Beyond MILP: Explore MINLP, hybrid approaches with constraint programming, and future trends in quantum optimization.

This handbook is designed to be the only MILP resource you’ll ever need — whether you are building optimization models in academia, applying advanced decision science in industry, or conducting research at the frontier of operations research.

Why choose this book?

• Comprehensive coverage: From basics to state-of-the-art techniques.

• Balanced learning: Theory + real code examples + applied case studies.

• Practical focus: Clear explanations and reproducible examples for immediate application.

If you want to solve complex optimization problems with confidence and stay ahead in the data-driven world, The MILP Optimization Handbook is your essential companion.

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