An introduction to Prospective Multiple Attribute Decision Making (PMADM). Issue 2 (3rd March 2016)

• Record Type: Journal Article
• Authors: Hashemkhani Zolfani, Sarfaraz; Maknoon, Reza; Zavadskas, Edmundas Kazimieras
• Is Part Of: Technological and economic development of economy. Volume 22:Issue 2(2016)
• Page Start: 309
• Page End: 326
• Publication Date: 2016-03-03
• Subjects: Prospective Multiple Attribute Decision Making (PMADM) -- decision making -- future -- MADM -- limiters; C02 -- C44 -- C61; Sustainable development -- Periodicals; Construction industry -- Technological innovations -- Lithuania -- Periodicals; Construction industry -- Capital productivity -- Lithuania -- Periodicals; Construction industry -- Capital productivity; Construction industry -- Technological innovations; Lithuania; Periodicals; 338.4762409479
• DOI: 10.3846/20294913.2016.1150363
• Languages: English
• ISSNs: 2029-4913

Abstract

Abstract: In recent years futures science has received a great deal of attention and has gained world-wide credibility in the science community as the science of tomorrows. The countless applications of futures studies in various fields have been a major breakthrough for mankind. Undoubtedly, decision making is one of the most significant aspects of shaping the future and an integral part of any credible future research. Multiple Criteria Decision Making (MCDM) in general and Multiple Attribute Decision Making in particular (MADM), are among the most remarkable subparts of the decision making process. The most recent model developed using the MADM method is the Dynamic MADM. The model does not specifically concentrate on the future actions and approaches and remains to be fully explored. This research presents a new concept and a new approach in the MADM field which is called the Prospective Multiple Attribute Decision Making (PMADM). The PMADM model can very well cover the DMADM concept but instead chooses to focus on future topics. The study also introduces two new approaches. The first research aims to elaborate the basis of this model and then evolves to deal with the future limiters as they potentially pop up and change the course of future actions. The new model based on future limiters is separated and categorized into two sections; one of which is looked upon without the probabilities rate and the other one with the probabilities rate. This approach is deemed priceless due to its major applicability in the ranking of the MADM methods such as: TOPSIS, VIKOR, COPRAS, ARAS, WASPAS and etc. Finally, a case study with the various applications of PMADM model in WASPAS methodology is put forth and illustrated.