Multiple Attribute Decision Making: An Introduction: Quantitative Applications in the Social Sciences, cartea 104
Autor K . Paul Yoon, Ching-Lai Hwangen Limba Engleză Paperback – 12 mar 1995
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Specificații
ISBN-13: 9780803954861
ISBN-10: 0803954867
Pagini: 83
Dimensiuni: 140 x 216 x 5 mm
Greutate: 0.1 kg
Ediția:New.
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
ISBN-10: 0803954867
Pagini: 83
Dimensiuni: 140 x 216 x 5 mm
Greutate: 0.1 kg
Ediția:New.
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
Cuprins
Introduction
Attribute Generation, Data and Weight
Noncompensatory Methods
Scoring Methods
TOPSIS
ELECTRE
Methods for Qualitative Data
Extensions
Attribute Generation, Data and Weight
Noncompensatory Methods
Scoring Methods
TOPSIS
ELECTRE
Methods for Qualitative Data
Extensions
Notă biografică
Dr Yoon received his Ph.D. degree from Kansas State University. He has taught courses on Quantitative Analysis, Operations Management, Quality Management, and Statistics. His research areas include Strategic Decision Analysis, Quality Management (Six Sigma), and Fuzzy Set Applications on Operations Management. Recently, he is working on user friendly translation of statistical principles.
Dr Yoon has published 30+ scholarly journal publications and three books, including the prestigious Sage Quantitative Applications in Social Sciences Series #104: Multiple Attribute Decision Making.
Dr Yoon spent one year as Visiting Research Associate Professor at Kansas State University. He has been a consultant to HTX International, HESI ,PSE&G and many more. He is a column writer for a daily Korean newspaper in NYC.
Descriere
This volume offers social scientists a concise overview of multiple attribute decision making (MADM) methods, their characteristics and applicability and methods for solving MADM problems. Real world examples are used to introduce the reader to normative models for optimal decisions.
The authors explore how MADM methods can be used for descriptive purposes to model: the existing decision-making process; noncompensatory and scoring methods; accommodation of soft data; construction of a multiple-decision support systems; and the validity of methods. The advanced procedures of TOPSIS and ELECTRE are also presented.