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  • Produktbild: Data Processing for the AHP/ANP
  • Produktbild: Data Processing for the AHP/ANP
Band 1

Data Processing for the AHP/ANP

Aus der Reihe Quantitative Management

Fr. 73.90

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.08.2014

Abbildungen

X, 138 p.

Verlag

Springer Berlin

Seitenzahl

138

Maße (L/B/H)

23.5/15.5/0.9 cm

Gewicht

236 g

Auflage

2013

Sprache

Englisch

ISBN

978-3-642-43380-1

Beschreibung

Rezension

From the reviews:

“Data Processing for the AHP/ANP focuses on the induced bias matrix model and its application in the AHP / ANP. … this book might interest … researchers in the AHP / ANP or, more generally, in decision-making procedures. … The book is well organized, introduces readers to the main methodological problems in applications of AHP / ANP models, and suggests how to use the IBMM to solve them.” (Josef Jablonsky, Interfaces, Vol. 43 (5), September-October, 2013)

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.08.2014

Abbildungen

X, 138 p.

Verlag

Springer Berlin

Seitenzahl

138

Maße (L/B/H)

23.5/15.5/0.9 cm

Gewicht

236 g

Auflage

2013

Sprache

Englisch

ISBN

978-3-642-43380-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Data Processing for the AHP/ANP
  • Produktbild: Data Processing for the AHP/ANP
  •  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 resourceallocation 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.