84,89 €
Extension of Data Envelopment Analysis with Preference Information
Extension of Data Envelopment Analysis with Preference Information
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Extension of Data Envelopment Analysis with Preference Information
Extension of Data Envelopment Analysis with Preference Information
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84,89 €
This book provides an introduction to the methods currently available in the field of Data Envelopment Analysis (DEA) to incorporate preference information. The book serves as a reference volume for the readers interested in those methods. In addition to theoretical considerations, numerous illustrative examples are included. Hence, the book can be used as a teaching text as well. Only a modest mathematical background is needed to understand the main principles. The only prerequisites are a) fa…
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This book provides an introduction to the methods currently available in the field of Data Envelopment Analysis (DEA) to incorporate preference information. The book serves as a reference volume for the readers interested in those methods. In addition to theoretical considerations, numerous illustrative examples are included. Hence, the book can be used as a teaching text as well. Only a modest mathematical background is needed to understand the main principles. The only prerequisites are a) familiarity with linear algebra especially matrix calculus, b) knowledge of the simplex method, and c) familiarity with the use of computer software.

This book is organized as follows. Chapter 1 provides motivation and introduces to the basic concepts. Chapter 2 provides the basic ideas and models of the Data Envelopment Analysis. The efficient frontier and production possibility set concepts play an important role in all considerations. That's why these concepts are considered closer in Chapter 3. Since the approaches introduced in this study are inspired by Multiple Objective Linear Programming, the basic concepts of this field are reviewed in Chapter 4. Chapter 5 also compares and contracts Data Envelopment Analysis and Multiple Objective Linear Programming providing some cornerstones for approaches presented later in this book. Chapter 6 discusses the traditional approaches to take into account preference information in DEA. In Chapter 7 Value Efficiency is introduced, and in Chapter 8 discusses practical aspects. Some extensions are presented in Chapter 9 and in Chapter 10 Value Efficiency is extended to cover the case, when a production possibility set is not convex. Three implemented applications are reviewed in Chapter 11.

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This book provides an introduction to the methods currently available in the field of Data Envelopment Analysis (DEA) to incorporate preference information. The book serves as a reference volume for the readers interested in those methods. In addition to theoretical considerations, numerous illustrative examples are included. Hence, the book can be used as a teaching text as well. Only a modest mathematical background is needed to understand the main principles. The only prerequisites are a) familiarity with linear algebra especially matrix calculus, b) knowledge of the simplex method, and c) familiarity with the use of computer software.

This book is organized as follows. Chapter 1 provides motivation and introduces to the basic concepts. Chapter 2 provides the basic ideas and models of the Data Envelopment Analysis. The efficient frontier and production possibility set concepts play an important role in all considerations. That's why these concepts are considered closer in Chapter 3. Since the approaches introduced in this study are inspired by Multiple Objective Linear Programming, the basic concepts of this field are reviewed in Chapter 4. Chapter 5 also compares and contracts Data Envelopment Analysis and Multiple Objective Linear Programming providing some cornerstones for approaches presented later in this book. Chapter 6 discusses the traditional approaches to take into account preference information in DEA. In Chapter 7 Value Efficiency is introduced, and in Chapter 8 discusses practical aspects. Some extensions are presented in Chapter 9 and in Chapter 10 Value Efficiency is extended to cover the case, when a production possibility set is not convex. Three implemented applications are reviewed in Chapter 11.

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