Fr. 109.00

New Methods and Applications in Multiple Attribute Decision Making (MADM)

English · Paperback / Softback

Shipping usually within 1 to 2 weeks (title will be printed to order)

Description

Read more

This book presents 27 methods of the Multiple Attribute Decision Making (MADM), which are not discussed in the existing books, nor studied in details, using more applications. Nowadays, decision making is one of the most important and fundamental tasks of management as an organizational goal achievement that depends on its quality. Decision making includes the correct expression of objectives, determining different and possible solutions, evaluating their feasibility, assessing the consequences, and the results of implementing each solution, and finally, selecting and implementing the solution. Multiple Criteria Decision Making (MCDM) is sum of the decision making techniques. MCDM is divided into the Multiple Objective Decision Making (MODM) for designing the best solution and MADM for selecting the best alternative. Given that the applications of MADM are mostly more than MODM, wide various techniques have been developed for MADM by researchers over the last 60 years, and the currentbook introduces some of the other new MADM methods.

     

List of contents

Chapter 1. SMART method.- Chapter 2. REGIME method.- Chapter 3. ORESTE method.- Chapter 4. VIKOR method.- Chapter 5. PROMETHEE I-II-III methods.- Chapter 6. QUALIFLEX method.- Chapter 7. SIR method.- Chapter 8. EVAMIX method.- Chapter 9. ARAS method.- Chapter 10. Taxonomy method.- Chapter 11. MOORA method.- Chapter 12. COPRAS method.- Chapter 13. WASPAS method.- Chapter 14. SWARA method.- Chapter 15. DEMATEL method.- Chapter 16. MACBETH method.- Chapter 17. ANP method.- Chapter 18. MAUT method.- Chapter 19. IDOCRIW method.- Chapter 20. TODIM method.- Chapter 21. EDAS method.- Chapter 22. PAMSSEM I & II.- Chapter 23. ELECTRE I-II-III methods.- Chapter 24. EXPROM I & II method.- Chapter 25. MABAC method.- Chapter 26. CRITIC Method.- Chapter 27. KEMIRA Method.

About the author










Alireza Alinezhad is an Iranian researcher that received his B.S. degree in Applied Mathematics from Iran University of Science and Technology, M.S. degree in Industrial Engineering from Tarbiat Modarres University, and Ph.D. degree in Industrial Engineering, from Islamic Azad University, Science and Research Branch. He is currently Associate Professor in the Department of Industrial Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran. His researches include Data Envelopment Analysis (DEA), Multiple Criteria Decision Making (MCDM), and quality engineering and management. 
 
Javad Khalili has M.Sc. in Industrial Engineering from Islamic Azad University of Qazvin. He received his bachelor degree in the field of Industry Engineering - Industrial Production in 2012 and his master degree in the field of Industrial Engineering - System Management and Productivity from Islamic Azad University of Qazvin, Iran, in 2017. His Master's thesis is entitled "Performance Evaluation in Aggregate Production Planning by Integrated Approach of DEA and Madm Under Uncertain Condition." His researches include Multiple Criteria Decision Making (MCDM), Data Envelopment Analysis (DEA), Supply Chain Management (SCM), and production planning. 



Product details

Authors Alirez Alinezhad, Alireza Alinezhad, Javad Khalili
Publisher Springer, Berlin
 
Languages English
Product format Paperback / Softback
Released 01.09.2020
 
EAN 9783030150112
ISBN 978-3-0-3015011-2
No. of pages 233
Dimensions 155 mm x 14 mm x 235 mm
Illustrations XXIV, 233 p. 114 illus., 2 illus. in color.
Series International Series in Operations Research & Management Science
Subject Natural sciences, medicine, IT, technology > Mathematics > Miscellaneous

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.