An Introduction to Robust Combinatorial Optimization: Concepts, Models and Algorithms for Decision Making under Uncertainty
Goerigk M, Hartisch M (2024)
Publication Type: Authored book
Publication year: 2024
Publisher: Springer
Series: International Series in Operations Research and Management Science
City/Town: Cham
Book Volume: 361
Pages Range: 1-300
ISBN: 978-3-031-61260-2
DOI: 10.1007/978-3-031-61261-9
Abstract
This book offers a self-contained introduction to the world of robust combinatorial optimization. It explores decision-making using the min-max and min-max regret criteria, while also delving into the two-stage and recoverable robust optimization paradigms. It begins by introducing readers to general results for interval, discrete, and budgeted uncertainty sets, and subsequently provides a comprehensive examination of specific combinatorial problems, including the selection, shortest path, spanning tree, assignment, knapsack, and traveling salesperson problems. The book equips both students and newcomers to the field with a grasp of the fundamental questions and ongoing advancements in robust optimization. Based on the authors’ years of teaching and refining numerous courses, it not only offers essential tools but also highlights the open questions that define this subject area.
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Germany (DE)How to cite
APA:
Goerigk, M., & Hartisch, M. (2024). An Introduction to Robust Combinatorial Optimization: Concepts, Models and Algorithms for Decision Making under Uncertainty. Cham: Springer.
MLA:
Goerigk, Marc, and Michael Hartisch. An Introduction to Robust Combinatorial Optimization: Concepts, Models and Algorithms for Decision Making under Uncertainty. Cham: Springer, 2024.
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