Analysis of rank reversal problems in "Weighted Aggregated Sum Product Assessment" method

Baykasoğlu A., Ercan E.

SOFT COMPUTING, cilt.25, sa.24, ss.15243-15254, 2021 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 24
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s00500-021-06405-w
  • Dergi Adı: SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.15243-15254
  • Anahtar Kelimeler: Multiple criteria analysis, WASPAS method, Rank reversal, Normalization, ANALYTIC HIERARCHY PROCESS, DECISION-MAKING, WASH CRITERION, WASPAS METHOD, SELECTION, SWARA, MODEL
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Multiple attribute decision-making (MADM) methods are commonly employed to assist decisions for selecting the best alternative according to conflicting criteria in complex decision situations. Although MADM methods have proven to be very useful, in some dynamic decision cases they may cause faulty results due to the "rank reversals" problem. Numerous MADM methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), and several others well-known methods proven to have rank reversal problems. In this paper, we study the rank reversal problem for a recent MADM method that is known as Weighted Aggregated Sum Product Assessment (WASPAS). As far as we know, there is no study that considers the rank reversal problem in the WASPAS. In this paper, rank reversal problems in WASPAS are analyzed empirically by considering different types of rank reversals. After detailed computational experiments, we show that rank reversal problems also exist in WASPAS when classical normalization techniques are utilized. We also show through extensive computational experiments by using different problem instances that rank reversal problems can be avoided in WASPAS when modified Max and Max-Min normalization techniques are used.