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Title
Multi-criteria and multi-dimensional analysis in decisions : decision making with Preference Vector Methods (PVM) and Vector Measure Construction Methods (VMCM) / Kesra Nermend.
Uniform Title
Metody wielokryterialnej i wielowymiarowej analizy we wspomaganiu decyzji. English
ISBN
9783031405389 (electronic bk.)
3031405382 (electronic bk.)
9783031405372
3031405374
Published
Cham : Springer, 2023.
Language
English
Description
1 online resource (xvi, 354 pages) : illustrations.
Item Number
10.1007/978-3-031-40538-9 doi
Call Number
HD30.23
Dewey Decimal Classification
658.4/03
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed November 8, 2023).
Series
Vector optimization, 1867-898X
Available in Other Form
Print version: 9783031405372
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.
