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Sprache: Englisch
Verlag: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031545494 ISBN 13: 9783031545498
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Hardcover. Zustand: new. Hardcover. This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to represent random phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models.This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are. The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty.Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York.Stein W. Wallace is a Professor of Operational Research and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty.Alan King is a Research Staff Member at IBM's Thomas J. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Sprache: Englisch
Verlag: Springer International Publishing AG, CH, 2024
ISBN 10: 3031545494 ISBN 13: 9783031545498
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Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031545494 ISBN 13: 9783031545498
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Taschenbuch. Zustand: Neu. Modeling with Stochastic Programming | Alan J. King (u. a.) | Taschenbuch | xvi | Englisch | 2014 | Springer | EAN 9781489992123 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This is an updated version of what is still the only text to address basic questions about how tomodel uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to representrandom phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models.This text would be suitable as astand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are.The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty.Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York.Stein W. Wallace is a Professor of Operational Research and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.
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Taschenbuch. Zustand: Neu. Modeling with Stochastic Programming | Alan J. King (u. a.) | Taschenbuch | xviii | Englisch | 2025 | Springer | EAN 9783031545528 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031545494 ISBN 13: 9783031545498
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Hardcover. Zustand: new. Hardcover. This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to represent random phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models.This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are. The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty.Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York.Stein W. Wallace is a Professor of Operational Research and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty.Alan King is a Research Staff Member at IBM's Thomas J. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Sprache: Englisch
Verlag: Springer International Publishing AG, CH, 2024
ISBN 10: 3031545494 ISBN 13: 9783031545498
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In den WarenkorbHardback. Zustand: New. Second Edition 2024.
Buch. Zustand: Neu. Modeling with Stochastic Programming | Alan J. King (u. a.) | Buch | Springer Series in Operations Research and Financial Engineering | xviii | Englisch | 2024 | Springer | EAN 9783031545498 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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