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An introduction to robust combinatorial optimization = concepts, models and algorithms for decision making under uncertainty /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to robust combinatorial optimization/ by Marc Goerigk, Michael Hartisch.
Reminder of title:	concepts, models and algorithms for decision making under uncertainty /
Author:	Goerigk, Marc.
other author:	Hartisch, Michael.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xii, 308 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Combinatorial optimization. -
Online resource:	https://doi.org/10.1007/978-3-031-61261-9
ISBN:	9783031612619

An introduction to robust combinatorial optimization = concepts, models and algorithms for decision making under uncertainty /
Goerigk, Marc.

An introduction to robust combinatorial optimizationconcepts, models and algorithms for decision making under uncertainty /[electronic resource] :by Marc Goerigk, Michael Hartisch. - Cham :Springer Nature Switzerland :2024. - xii, 308 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3612214-7934 ;. - International series in operations research & management science ;v. 361..

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.

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.

ISBN: 9783031612619

Standard No.: 10.1007/978-3-031-61261-9doiSubjects--Topical Terms:

544215
Combinatorial optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.64

An introduction to robust combinatorial optimization = concepts, models and algorithms for decision making under uncertainty /
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505 0 $a 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.
520 $a 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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