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An Introduction to Robust Combinatorial Optimization
Concepts, Models and Algorithms for Decision Making under Uncertainty
Buch von Michael Hartisch (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor

Marc Goerigk is a Professor and Chair of Business Decisions and Data Science at the University of Passau, Germany. He has previously held positions at the Universities of Siegen, Lancaster (UK), Kaiserslautern, and Göttingen, where he pursued his studies in mathematics. Marc has a keen interest in optimization under uncertainty.

Michael Hartisch currently serves as a temporary professor of Analytics & Mixed-Integer Optimization at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany. Prior to this role, he was acting chair of Network and Data Science Management at the University of Siegen, Germany. His academic journey began with studies in mathematics at Friedrich Schiller University Jena, Germany. Michael's primary focus is on optimization under uncertainty.

Inhaltsverzeichnis

1. Introduction.- 2. Basic Concepts.- 3. Robust Problems.- 4. General Reformulation Results.- 5. General Solution Methods.- 6. Robust election Problems.- 7. Robust Shortest Path Problems.- 8. Robust Spanning Tree Problems.- 9. Other Combinatorial Problems.- 10. Other Models for Robust Optimization.- 11. Open Problems.

Details
Erscheinungsjahr: 2024
Fachbereich: Allgemeines
Genre: Recht, Sozialwissenschaften, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Buch
Inhalt: xii
308 S.
87 s/w Illustr.
308 p. 87 illus.
ISBN-13: 9783031612602
ISBN-10: 3031612604
Sprache: Englisch
Einband: Gebunden
Autor: Hartisch, Michael
Goerigk, Marc
Hersteller: Springer Nature Switzerland
Springer International Publishing AG
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 23 mm
Von/Mit: Michael Hartisch (u. a.)
Erscheinungsdatum: 23.08.2024
Gewicht: 0,647 kg
Artikel-ID: 129029073
Über den Autor

Marc Goerigk is a Professor and Chair of Business Decisions and Data Science at the University of Passau, Germany. He has previously held positions at the Universities of Siegen, Lancaster (UK), Kaiserslautern, and Göttingen, where he pursued his studies in mathematics. Marc has a keen interest in optimization under uncertainty.

Michael Hartisch currently serves as a temporary professor of Analytics & Mixed-Integer Optimization at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany. Prior to this role, he was acting chair of Network and Data Science Management at the University of Siegen, Germany. His academic journey began with studies in mathematics at Friedrich Schiller University Jena, Germany. Michael's primary focus is on optimization under uncertainty.

Inhaltsverzeichnis

1. Introduction.- 2. Basic Concepts.- 3. Robust Problems.- 4. General Reformulation Results.- 5. General Solution Methods.- 6. Robust election Problems.- 7. Robust Shortest Path Problems.- 8. Robust Spanning Tree Problems.- 9. Other Combinatorial Problems.- 10. Other Models for Robust Optimization.- 11. Open Problems.

Details
Erscheinungsjahr: 2024
Fachbereich: Allgemeines
Genre: Recht, Sozialwissenschaften, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Buch
Inhalt: xii
308 S.
87 s/w Illustr.
308 p. 87 illus.
ISBN-13: 9783031612602
ISBN-10: 3031612604
Sprache: Englisch
Einband: Gebunden
Autor: Hartisch, Michael
Goerigk, Marc
Hersteller: Springer Nature Switzerland
Springer International Publishing AG
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 23 mm
Von/Mit: Michael Hartisch (u. a.)
Erscheinungsdatum: 23.08.2024
Gewicht: 0,647 kg
Artikel-ID: 129029073
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