Data Processing for the AHP/ANP

• Κύριοι συγγραφείς: Kou, Gang (Συγγραφέας), Ergu, Daji (Συγγραφέας), Peng, Yi (Συγγραφέας), Shi, Yong (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Σειρά: Quantitative Management, 1
• Θέματα: Business.; Operations research.; Decision making.; Business and Management.; Operation Research/Decision Theory.

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...

Πίνακας περιεχομένων:

•  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.