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)This book examines issues of PCM, including consistency test, inconsistent data identification and adjustment, missing or uncertain data estimation, and sensitivity analysis of rank reversal. Proposes and demonstrates an induced bias matrix model (IBMM).
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Destinos, gastos y plazos de envíoLibrería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 148 pp. Englisch. Nº de ref. del artículo: 9783642433801
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783642433801
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A timely report of state-of-the art analytical techniques An analysis of different methods such as an induced bias matrix model (IBMM) for the data processing A method of missing item score estimation in questionnaire A proposal of i. Nº de ref. del artículo: 5060739
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 148 pp. Englisch. Nº de ref. del artículo: 9783642433801
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