A New Index for TOPSIS based on Relative Distance to Best and Worst Points

S. A. Sadabadi, A. Hadi-Vencheh, A. Jamshidi and M. Jalali
Additional contact information
S. A. Sadabadi: Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
A. Hadi-Vencheh: Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
A. Jamshidi: Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
M. Jalali: Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 03, 695-719

Abstract: The technique for order performance by similarity to ideal solution (TOPSIS) is one of the most well-known methods in multiple criteria decision making (MCDM) problems. The classical TOPSIS method employs a similarity index to rank alternatives. However, the chosen alternative sometimes does not have the shortest distance to the positive ideal solution (PIS) and remotest distance from the negative ideal solution (NIS), simultaneously. Besides, in some cases, TOPSIS cannot assign a unique rank to alternatives. The purpose of this paper is to propose a new similarity TOPSIS index based on the relative distance to the best and worst points. In the proposed method, by treating the separations of an alternative from the PIS and the NIS as negative criterion and positive criterion, respectively, we reduce the original MCDM problem to a new one with two criteria. The proposed index, based on different weights, in optimistic, pessimistic, and apathetic cases, easily determines the score of each alternative. Finally, we illustrate the proposed index using four numerical examples. The results are compared with those published in the literature.

Keywords: Multiple criteria decision making (MCDM); TOPSIS; relative distance; best point; worst point (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622020500145
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:19:y:2020:i:03:n:s0219622020500145

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622020500145

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().