Multi-Criteria and Multi-Dimensional Analysis in Decisions: Decision Making with Preference Vector Methods (PVM) and Vector Measure Construction Methods (VMCM)
Nermend, Kesra.
Multi-Criteria and Multi-Dimensional Analysis in Decisions: Decision Making with Preference Vector Methods (PVM) and Vector Measure Construction Methods (VMCM) by Kesra Nermend - 1st ed 2023 - 1 recurso en línea - Vector Optimization, 1867-898X . - Vector Optimization, .
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.
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.
9783031405389
10.1007/978-3-031-40538-9 doi
Research.
Economics, Mathematical.
Methodologie.
T57.97 / 2023 EB
Multi-Criteria and Multi-Dimensional Analysis in Decisions: Decision Making with Preference Vector Methods (PVM) and Vector Measure Construction Methods (VMCM) by Kesra Nermend - 1st ed 2023 - 1 recurso en línea - Vector Optimization, 1867-898X . - Vector Optimization, .
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.
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.
9783031405389
10.1007/978-3-031-40538-9 doi
Research.
Economics, Mathematical.
Methodologie.
T57.97 / 2023 EB