Multi-Criteria and Multi-Dimensional Analysis in Decisions

Decision Making with Preference Vector Methods (PVM) and Vector Measure Construction Methods (VMCM)

Häftad, Engelska, 2024

Av Kesra Nermend

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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.

Produktinformation

Utgivningsdatum2024-11-02
Mått155 x 235 x 21 mm
Vikt563 g
FormatHäftad
SpråkEngelska
SerieVector Optimization
Antal sidor354
FörlagSpringer International Publishing AG
ISBN9783031405402
OriginaltitelMetody wielokryterialnej i wielowymiarowej analizy we wspomaganiu decyzji

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Kesra Nermend is Professor and Head of the Department of Decision Support Methods and Cognitive Neuroscience; and President of the Centre for Knowledge and Technology Transfer at the Institute of Management, University of Szczecin (Szczecin, Poland). His scientific interests are related to the use of quantitative methods and IT tools in the analysis of socio-economic processes, with particular emphasis on multi-criteria methods, multidimensional data analysis, cognitive neuroscience techniques in researching social behavior and modeling consumer preference in the process of making business decisions. He has published over 130 publications in Polish and English languages including 20 monographs.

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.