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Foundations of Robust Optimization
Decision-Making Under Uncertainty
This book is 100% completeLast updated on 2026-08-29
Build optimization models that remain useful when forecasts are wrong. This practical guide connects uncertainty sets, robust counterparts, duality, budgets of uncertainty, and modern distributionally robust optimization with real operational applications and implementation advice.
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Foundations of Robust Optimization offers a practical, mathematically grounded introduction to decision-making under uncertainty. It explains how to formulate uncertainty sets, derive tractable robust counterparts, use duality and budgets of uncertainty, and connect classical robust optimization with distributionally robust optimization.
Designed for graduate students, operations researchers, analysts, and engineering practitioners, the book develops these ideas through linear, conic, and two-stage models, with applications to logistics, battery recovery and recycling, and EV charging infrastructure. It also provides implementation checklists, solver-selection guidance, validation principles, exercises, and selected references.
Readers will learn not only how to build robust models, but also how to state their guarantees correctly, measure the cost of protection, test decisions out of sample, and recognize common modeling and computational pitfalls.
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