Data Processing for the AHP/ANP

Gespeichert in:

Personen und Körperschaften:	Kou, Gang (VerfasserIn), Daji Ergu (Sonstige), Peng, Yi (Sonstige), Shi, Yong (Sonstige)
Titel:	Data Processing for the AHP/ANP/ by Gang Kou, Daji Ergu, Yi Peng, Yong Shi
Format:	E-Book
Sprache:	Englisch
veröffentlicht:	Berlin, Heidelberg Springer 2013
Gesamtaufnahme:	Quantitative Management SpringerLink
Schlagwörter:	Economics Economics/Management Science Operations research
Buchausg. u.d.T.:	Data processing for the AHP/ANP, 1. ed., Berlin : Springer, 2013, X, 136 S.
Quelle:	Verbunddaten SWB


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505	8	0	\|a Quantitative Management for theAHP/ANP; Abstract; Keywords; Contents; Chapter 1: Introduction; References; Chapter 2: A New Consistency Test Index for the Data in the AHP/ANP; 2.1 Basics of the AHP/ANP; 2.1.1 The Positive 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
505	8	0	\|a 2.3.1 The Advantages of Maximum Eigenvalue Threshold for AHP/ANP2.4 The Processes of Data Consistency Test in AHP/ANP; 2.5 Illustrative Example; References; Chapter 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; 3.2.2.1 Method of Matrix Order Reduction
505	8	0	\|a 3.2.3 Fast Inconsistency Identification and Adjustment Method3.2.3.1 Method of Non-Zero Rows (Columns) and Signs Identification; 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; References; Chapter 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
505	8	0	\|a 4.4.1 Illustrative Examples in Order Three4.4.2 Illustrative Examples in Order Four; References; 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; References; 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; References; Chapter 7: Applications of IBMM; 7.1 Task Scheduling and Resource Allocation in Cloud Computing Environment by the IBMM
505	8	0	\|a 7.1.1 Resource Allocation in Cloud Computing7.1.2 Task-Oriented Resource Allocation in Cloud Computing; 7.1.3 Illustrative Examples; 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; References; Chapter 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; Index
520			\|a 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
520			\|a 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 adjustment, data collection, missing or uncertain data estimation, and sensitivity analysis of rank reversal. The maximum eigenvalue threshold method is proposed as the new consistency index for the AHP/ANP. An induced bias matrix model (IBMM) is proposed to identify and adjust the inconsistent data, and estimate the missing or uncertain data. Two applications of IBMM including risk assessment and decision analysis, task scheduling and resource allocation in cloud computing environment, are introduced to illustrate the proposed IBMM
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contents	Quantitative Management for theAHP/ANP; Abstract; Keywords; Contents; Chapter 1: Introduction; References; Chapter 2: A New Consistency Test Index for the Data in the AHP/ANP; 2.1 Basics of the AHP/ANP; 2.1.1 The Positive 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 AHP/ANP2.4 The Processes of Data Consistency Test in AHP/ANP; 2.5 Illustrative Example; References; Chapter 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; 3.2.2.1 Method of Matrix Order Reduction, 3.2.3 Fast Inconsistency Identification and Adjustment Method3.2.3.1 Method of Non-Zero Rows (Columns) and Signs Identification; 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; References; Chapter 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 Three4.4.2 Illustrative Examples in Order Four; References; 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; References; 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; References; Chapter 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 Computing7.1.2 Task-Oriented Resource Allocation in Cloud Computing; 7.1.3 Illustrative Examples; 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; References; Chapter 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; Index, 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, 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 adjustment, data collection, missing or uncertain data estimation, and sensitivity analysis of rank reversal. The maximum eigenvalue threshold method is proposed as the new consistency index for the AHP/ANP. An induced bias matrix model (IBMM) is proposed to identify and adjust the inconsistent data, and estimate the missing or uncertain data. Two applications of IBMM including risk assessment and decision analysis, task scheduling and resource allocation in cloud computing environment, are introduced to illustrate the proposed IBMM
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spelling	Kou, Gang (DE-588)140231773 (DE-627)616526946 (DE-576)315424672 aut, Data Processing for the AHP/ANP by Gang Kou, Daji Ergu, Yi Peng, Yong Shi, Berlin Heidelberg Springer 2013, Online-Ressource (X, 138 p. 22 illus, digital), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Quantitative Management 1, SpringerLink Bücher, Description based upon print version of record, Quantitative Management for theAHP/ANP; Abstract; Keywords; Contents; Chapter 1: Introduction; References; Chapter 2: A New Consistency Test Index for the Data in the AHP/ANP; 2.1 Basics of the AHP/ANP; 2.1.1 The Positive 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 AHP/ANP2.4 The Processes of Data Consistency Test in AHP/ANP; 2.5 Illustrative Example; References; Chapter 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; 3.2.2.1 Method of Matrix Order Reduction, 3.2.3 Fast Inconsistency Identification and Adjustment Method3.2.3.1 Method of Non-Zero Rows (Columns) and Signs Identification; 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; References; Chapter 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 Three4.4.2 Illustrative Examples in Order Four; References; 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; References; 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; References; Chapter 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 Computing7.1.2 Task-Oriented Resource Allocation in Cloud Computing; 7.1.3 Illustrative Examples; 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; References; Chapter 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; Index, 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, 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 adjustment, data collection, missing or uncertain data estimation, and sensitivity analysis of rank reversal. The maximum eigenvalue threshold method is proposed as the new consistency index for the AHP/ANP. An induced bias matrix model (IBMM) is proposed to identify and adjust the inconsistent data, and estimate the missing or uncertain data. Two applications of IBMM including risk assessment and decision analysis, task scheduling and resource allocation in cloud computing environment, are introduced to illustrate the proposed IBMM, Economics, Economics/Management Science, Operations research, s (DE-588)4113446-1 (DE-627)105829420 (DE-576)209476559 Entscheidungsfindung gnd, s (DE-588)4225065-1 (DE-627)104981881 (DE-576)210303336 Analytic Hierarchy Process gnd, DE-101, Daji Ergu (DE-588)1013881982 (DE-627)664863787 (DE-576)347907148 oth, Peng, Yi (DE-588)135755743 (DE-627)571243762 (DE-576)300629621 oth, Shi, Yong (DE-627)1436387973 (DE-576)366387979 oth, 9783642292125, Buchausg. u.d.T. Data processing for the AHP/ANP 1. ed. Berlin : Springer, 2013 X, 136 S. (DE-627)688617980 (DE-576)371068177 3642292127 9783642292125, https://doi.org/10.1007/978-3-642-29213-2 X:SPRINGER Verlag Volltext, http://dx.doi.org/10.1007/978-3-642-29213-2 Resolving-System lizenzpflichtig Volltext, https://swbplus.bsz-bw.de/bsz373499043cov.jpg V:DE-576 X:springer image/jpeg 20140131132508 Cover, http://d-nb.info/102043645X/04 B:DE-101 application/pdf 2013-05-01 Verlag Inhaltsverzeichnis, http://deposit.d-nb.de/cgi-bin/dokserv?id=3989356&prov=M&dok_var=1&dok_ext=htm X: MVB text/html 2013-05-01 Verlag Inhaltstext, (DE-627)728219662, http://dx.doi.org/10.1007/978-3-642-29213-2 DE-Zwi2, DE-Zwi2 2013-03-27T15:58:24Z 2017-03-13, http://dx.doi.org/10.1007/978-3-642-29213-2 DE-520, DE-520 2012-12-21T09:53:24Z
spellingShingle	Kou, Gang, Data Processing for the AHP/ANP, Quantitative Management for theAHP/ANP; Abstract; Keywords; Contents; Chapter 1: Introduction; References; Chapter 2: A New Consistency Test Index for the Data in the AHP/ANP; 2.1 Basics of the AHP/ANP; 2.1.1 The Positive 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 AHP/ANP2.4 The Processes of Data Consistency Test in AHP/ANP; 2.5 Illustrative Example; References; Chapter 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; 3.2.2.1 Method of Matrix Order Reduction, 3.2.3 Fast Inconsistency Identification and Adjustment Method3.2.3.1 Method of Non-Zero Rows (Columns) and Signs Identification; 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; References; Chapter 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 Three4.4.2 Illustrative Examples in Order Four; References; 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; References; 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; References; Chapter 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 Computing7.1.2 Task-Oriented Resource Allocation in Cloud Computing; 7.1.3 Illustrative Examples; 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; References; Chapter 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; Index, 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, 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 adjustment, data collection, missing or uncertain data estimation, and sensitivity analysis of rank reversal. The maximum eigenvalue threshold method is proposed as the new consistency index for the AHP/ANP. An induced bias matrix model (IBMM) is proposed to identify and adjust the inconsistent data, and estimate the missing or uncertain data. Two applications of IBMM including risk assessment and decision analysis, task scheduling and resource allocation in cloud computing environment, are introduced to illustrate the proposed IBMM, Economics, Economics/Management Science, Operations research, Entscheidungsfindung, Analytic Hierarchy Process
swb_id_str	373499043
title	Data Processing for the AHP/ANP
title_auth	Data Processing for the AHP/ANP
title_full	Data Processing for the AHP/ANP by Gang Kou, Daji Ergu, Yi Peng, Yong Shi
title_fullStr	Data Processing for the AHP/ANP by Gang Kou, Daji Ergu, Yi Peng, Yong Shi
title_full_unstemmed	Data Processing for the AHP/ANP by Gang Kou, Daji Ergu, Yi Peng, Yong Shi
title_short	Data Processing for the AHP/ANP
title_sort	data processing for the ahp anp
topic	Economics, Economics/Management Science, Operations research, Entscheidungsfindung, Analytic Hierarchy Process
topic_facet	Economics, Economics/Management Science, Operations research, Entscheidungsfindung, Analytic Hierarchy Process
url	https://doi.org/10.1007/978-3-642-29213-2, http://dx.doi.org/10.1007/978-3-642-29213-2, https://swbplus.bsz-bw.de/bsz373499043cov.jpg, http://d-nb.info/102043645X/04, http://deposit.d-nb.de/cgi-bin/dokserv?id=3989356&prov=M&dok_var=1&dok_ext=htm