Production Planning by Mixed Integer Programming
Yves Pochet|Laurence A. Wolsey
Language: English
Published by Springer New York, 2010
Series: Book 7 of 43 - Springer Series in Operations Research and Financial Engineering
- Softcover
- New
Condition: New
£ 69.90
Quantity: Over 20 available
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First to collect the large number of mathematical (polyhedral combinatorics) results on lotsizing and single product planning problems developed over the last twenty yearsAuthors are world leaders in mixed integer programming Tutorial style.
Seller Inventory # 4172664
- Title
- Production Planning by Mixed Integer Programming
- Author
- Yves Pochet|Laurence A. Wolsey
- Publisher
- Springer New York
- Publication year
- 2010
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 144192132X
- ISBN 13
- 9781441921321
- Series
- Book 7 of 43: Springer Series in Operations Research and Financial Engineering
This textbook provides a comprehensive modeling, reformulation and optimization approach for solving production planning and supply chain planning problems, covering topics from a basic introduction to planning systems, mixed integer programming (MIP) models and algorithms through the advanced description of mathematical results in polyhedral combinatorics required to solve these problems.
Based on twenty years worth of research in which the authors have played a significant role, the book addresses real life industrial production planning problems (involving complex production structures with multiple production stages) using MIP modeling and reformulation approach.
The book provides an introduction to MIP modeling and to planning systems, a unique collection of reformulation results, and an easy to use problem-solving library. This approach is demonstrated through a series of real life case studies, exercises and detailed illustrations.
Review by Jakub Marecek (Computer Journal) The emphasis put on mixed integer rounding and mixing sets, heuristics in-built in general purpose integer programming solvers, as well as on decompositions and heuristics using integer programming should be praised... There is no doubt that this volume offers the present best introduction to integer programming formulations of lotsizing problems, encountered in production planning. (2007)
"Synopsis" may belong to another edition of this title.
From the Back Cover
This textbook provides a comprehensive modeling, reformulation and optimization approach for solving production planning and related supply chain planning problems, covering topics from a basic introduction to planning systems, mixed integer programming (MIP) models and algorithms through the advanced description of mathematical results in polyhedral combinatorics required to solve these problems. This book addresses the solution of real life or industrial production planning problems (involving complex production structures with multiple production stages) using a MIP modeling and reformulation approach. It is based on close to twenty years of research in which the authors have played a significant role. One of the goals of this book is to allow non-expert readers, students in business, engineering, applied mathematics and computer science to solve such problems using standard modeling tools and MIP software. To achieve this the book provides a unique collection of reformulation results, integrating them into a comprehensive modeling and reformulation approach, as well as an easy to use problem-solving library. Moreover this approach is demonstrated through a series of real life case studies, exercises and detailed illustrations.
Graduate students and researchers in operations research, management, science and applied mathematics wishing to gain a deeper understanding of the formulations and mathematics underlying this approach will find this book useful because of its detailed treatment of the polyhedral structure of the basic lot-sizing problems and simple mixed integer sets that arise in the decomposition of more complicated problems. This book will allow the reader to improve formulations of non-standard MIP models and produce state-of-the-art models and algorithms.
"About the title" may belong to another edition of this title.
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