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Multi-Criteria and Multi-Dimensional Analysis in Decisions Decision Making with Preference Vector Methods (PVM) and Vector Measure Construction Methods (VMCM)
Author
Title
Multi-Criteria and Multi-Dimensional Analysis in Decisions [electronic resource] : Decision Making with Preference Vector Methods (PVM) and Vector Measure Construction Methods (VMCM) / by Kesra Nermend.
ISBN
9783031405389
Edition
1st ed. 2023.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Physical Description
1 online resource (XVI, 354 p.) 120 illus.
Local Notes
Access is available to the Yale community.
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Access restricted by licensing agreement.
Summary
A new era is emerging in which a group of quantitative methods featuring characteristics of multidimensional comparative analysis (MCA) and multi-criteria decision-making analysis (MCDA) can be used to automate objective decision-making processes. This book introduces the character of the criteria (desirable, non-desirable, motivating, demotivating, and neutral) to MCDA and MCA methods. It presents the author's own developed methods, the preference vector method (PVM), for solving multi-criteria problems in decision making; and, vector measure construction method (VMCM), which is dedicated to solving typical problems in the field of multidimensional comparative analysis. All methods are explained step by step with relevant examples, primarily in the fields of economics and management.
Variant and related titles
Springer ENIN.
Other formats
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Printed edition:
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Format
Books / Online
Language
English
Added to Catalog
November 02, 2023
Series
Vector Optimization,
Contents
Chapter 1 Introduction
Chapter 2 Problems of multi-criteria and multidimensionality in decision support
Part I: Methods of multidimensional comparative analysis
Chapter 3 Initial data analysis procedure
Chapter 4 Methods for building aggregate measures
Part II: Multi-criteria decision support methods
Chapter 5 Methods based on the outranking relationship
Chapter 6 Methods based on the utility function
Chapter 7 Multi-criteria methods using function points
Chapter 8 Conclusions.
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