Fuzzy multi-attribute cost–benefit analysis of e-services

Abstract

E-service evaluation is a complex problem in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Cost-benefit analyses applied to various areas are usually based on the data under certainty or risk. In case of uncertain, vague, and/or linguistic data, the fuzzy set theory can be used to handle the analysis. In this article, after the evaluation attributes of e-services and the fuzzy multi-attribute decisionmaking methods are introduced, a fuzzy hierarchical TOPSIS model is developed and applied to an e-service provider selection problem with some sensitivity analyses. The developed model is a useful tool for the companies that prefer outsourcing for e-activities. It is shown that service systems can be effectively evaluated by the proposed method.

Group decision making process for RFID-based system selection using fuzzy TOPSIS approach

“…In 2007, Kahraman and his research team proposed a hierarchical fuzzy TOPSIS method that has ability to consider the hierarchy among the attributes and alternatives. This method provides greater superiority to classical fuzzy TOPSIS methods [43] . Other researchers have employed TOPSIS and applied that to areas as such as company financial ratios comparison [56] , facility location selection [46] , assessment of service quality in airline industry [57] , materials selection [58] , manufacturing plant location analysis [59,60] , and Robot selection [61] , to mention a few.…”

“…There are ways to rank competitive alternatives but ranking competing alternatives in terms of their overall performance with respect to some criterions in fuzzy environment is made possible by the use of fuzzy TOPSIS methodology. TOPSIS treats a multi attribute decision making problem with m alternatives as a geometric system with m points in the n-dimensional space [43] . The ranking of alternatives in TOPSIS is based upon 'the relative similarity to the ideal solution point', which avoids from the situation of having same similarity to both ideal and negative ideal solutions points.…”

Effect of Human Behaviour in Shipboard Firefighting Decisions: The Case of Fire in Engine Rooms

“…The weight of each consequence is the outcome of experts' judgements while the values of the measures depend on the feedbacks of the person who uses the tool. Regarding the weights of the priorities, many researchers who carried out a sensitivity analysis for the AHP method suggest that small changes in weights, such as 20%, should not cause a rank reversal (Chang, Wu, Lin & Chen, ; Kahraman, Ates, Çevik & Gülbay, ). Such a change in the weight will be caused when priorities will be re‐adjusted.…”

The Characteristic Objects Method: A New Distance‐based Approach to Multicriteria Decision‐making Problems

“…In no time, the TOPSIS method was applied in various scientific fields, and recently, the global interest in the TOPSIS method has exponentially grown (Behzadian et al, 2012). For instance, in supply chain management and logistics, the TOPSIS approach was used for supplier selection (Chen et al, 2006;Boran et al, 2009;Celik, 2010;Chen, 2011;Dalalah et al, 2011;Deng and Chan, 2011;Fazlollahtabar et al, 2011), the location problem (Chu, 2002;Yong, 2006;Ertuğrul, 2010;Kuo, 2011;Awasthi et al, 2011;Li et al, 2011) and the selecting service problems (Bottani and Rizzi, 2006;Kahraman et al, 2007;Lin and Tsai, 2009;Chamodrakas et al, 2011;Cheng et al, 2011). It was also used in energy management (Yan et al, 2011;Kaya and Kahraman, 2011;Boran et al, 2012), chemical engineering (Rao and Baral, 2011;Sun et al, 2011;Ramezani et al, 2011), health problem (Rahimi et al, 2007;Wang et al, 2010;La Scalia et al, 2011) and even for the new ground-air missile weapon system selection problem (Li et al, 2010).…”