Data processing for the AHP/ANP

Data processing for the AHP/ANP

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The positive reciprocal pairwise comparison matrix (PCM) is one of the key components which is used to quantify the qualitative and/or intangible attributes into measurable quantities. This book examines six understudied issues of PCM, i.e.  consistency test, inconsistent data identification and adj...

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Detalles Bibliográficos
Otros Autores: Kou, Gang (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: New York : Springer 2013.
Edición:1st ed. 2013.
Colección:Quantitative Management, 1
Materias:
Decision making.
Decision making > Data processing.
Business > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009468724006719
Tabla de Contenidos:
  •  1: Introduction
  • 2: A new consistency test index for the data in the AHP/ANP.-  2.1 Basics of the AHP/ANP
  • 2.1.1 The reciprocal pairwise comparison matrix
  • 2.1.2 Basics of the AHP
  • 2.1.3 Basics of the ANP
  • 2.2 Consistency test issue in the AHP/ANP.-  2.2.1. Analysis of the consistency ratio (CR) method
  • 2.2.2 The issues of consistency test in the AHP/ANP
  • 2.3 The new consistency index——Maximum Eigenvalue Threshold for the AHP/ANP
  • 2.3.1 The advantages of Maximum Eigenvalue Threshold for the AHP/ANP
  • 2.4 The processes of data consistency test in the AHP/ANP
  • 2.5. Illustrative example
  • 3: IBMM for inconsistent data identification and adjustment in the AHP/ANP
  • 3.1 The theorems of induced bias matrix model (IBMM)
  • 3.1.1 The theoretical proofs of IBMM
  • 3.2 IBMM for inconsistent data identification and adjustment
  • 3.2.1 The basics of the inconsistency identification and adjustment method
  • 3.2.2. The processes of inconsistency identification and adjustment method
  • 3.2.3 Fast inconsistency identification and adjustment method
  • 3.3. Illustrative examples
  • 3.3.1 Illustrative examples for general inconsistency identification and adjustment method
  • 3.3.2 Illustrative examples for fast inconsistency identification and adjustment method
  • 4: IBMM for Missing Data Estimation
  • 4.1 Basics of the IBMM for missing data estimation
  • 4.2 The processes of estimating missing data by the IBMM
  • 4.3 Proofs of the IBMM for IPCM in order three
  • 4.4 Illustrative examples
  • 4.4.1 Illustrative examples in order three
  • 4.4.2 Illustrative examples in order four
  • Chapter 5: IBMM for Questionnaire Design Improvement
  • 5.1 Motivation of the research
  • 5.2 The principles of improving the questionnaire design
  • 5.3 Illustrative example
  • Chapter 6: IBMM for rank reversal
  • 6.1 Rank reversal issue in the AHP/ANP
  • 6.2 Sensitivity analysis of rank reversal by the IBMM
  • 6.3 Illustrative examples
  • 7: Applications of IBMM
  • 7.1 Task scheduling and resource allocation in cloud computing environment by the IBMM
  • 7.1.1 Resource allocation in cloud computing
  • 7.1.2 Task-oriented resource allocation in cloud computing
  • 7.1.3 Illustrative example
  • 7.2 Risk assessment and decision analysis by the IBMM
  • 7.2.1 Background of risk assessment and decision analysis
  • 7.2.2 Illustrative Examples
  • 8. Induced Arithmetic Average Bias Matrix Model (IAABMM)
  • 8.1 The theorem of IAABMM
  • 8.2 The inconsistency identification processes of IAABMM
  • 8.3 The estimating formula of inconsistency adjustment
  • 8.4. Illustrative Examples
  • References.

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