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.
This book presents 27 methods of the Multiple Attribute Decision Making (MADM), which are not discussed in existing books, nor studied in details, using various 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 current book introduces some of the other new MADM methods.