Author Description

The MILP Handbook: Practical Modeling with Mixed-Integer Linear Programming Real-world modeling techniques. No fluff. No proofs. Just practical optimization. Optimization is one of the most powerful tools for turning messy, complex decisions into clear, structured plans. But most people struggle to connect the theory they’ve seen in class with the models they actually need to build. That’s where The MILP Handbook comes in. This book is part of a practical series designed for professionals who want to build effective, efficient, and production-ready optimization models without getting lost in algorithms or academic abstractions. What You’ll Learn: This hands-on primer builds a strong foundation in both linear programming (LP) and mixed-integer programming (MIP), helping you turn business logic into mathematical models that solve fast and scale well. Chapter 1: Fundamentals of Mathematical Modeling Learn the structure of optimization models (variables, objectives, constraints) and how solvers interpret them. Includes practical tips on duality, feasibility, and numerical stability. Chapter 2: Primer on Linear Programming Understand the LP modeling workflow, from writing clean formulations to interpreting solver behavior. Includes comparisons between simplex and barrier methods and practical solver tricks. Chapter 3: Primer on Mixed Integer Programming Extend LP models to handle discrete choices. Covers branch-and-bound, presolve, cuts, gap tolerances, and how different enhancements impact performance in practice. Chapter 4: Network Flow Models Covers shortest path, max flow, min-cost flow, transportation, and transshipment problems; showing why network flows are so powerful and how to model them effectively. Chapter 5: Sensitivity Analysis Learn to interpret solver output and make better decisions using shadow prices, reduced costs, sensitivity ranges, and dual variables, even in MIP models. Who This Book Is For: Practitioners wh