- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
In this paper, we propose a Generalised-Fuzzy-TOPSIS method as a versatile evaluation model. The model is suitable for different types of fuzzy or interval-valued numbers, with or without subjective weights of criteria being defined by evaluators. Additionally, we extend the final ranking step of the TOPSIS method, which is the calculation of closeness coefficient based on the separation from Negative Ideal Solution (NIS) and proximity to Positive Ideal Solution (PIS). Experiments show that with the same focus on PIS and NIS distances, our proposed ranking is identical to TOPSIS, and also performs very well when varying the distance weights. The applicability of the proposed method is demonstrated with relevant examples of technology and material selection in the context of additive manufacturing. Sensitivity analyses, based on subjective weights of criteria, degree of optimism, evaluators‟ weights in group decision making, and distance weights, are presented to assist managers in making more informed decisions.
6. Discussion and Conclusion
The study, to the best of the authors‟ knowledge, is a novel generalisable model to select the best alternative in an uncertain environment. Additionally, we have shown that IFS, IVFS, FS are special cases of the proposed method based on IVIFS. We have demonstrated how to use different preference numbers with two practical examples of AM: technology and material selections. We have combined the uncertainty entropy weights with subjective weights, to present an intelligent and flexible model. The study further introduces the degree of optimism, which gives a choice to the evaluator to check performance sensitivity and ascertain its effect on ranking. This study, therefore, addresses the criticism by Opricovic & Tzeng (2004), that TOPSIS method does not take care of different weights of NIS and PIS distances, by extending the calculation of closeness coefficient to incorporate the different distance weights.
The application of GFTOPSIS method is demonstrated for AM technology selection and material selection. AM is an emerging technology which has attracted the attention of researchers and practitioners, for finding solutions to problems arising in newer and potential applications of AM in manufacturing and supply chain management. The increase in the usage of AM has been facilitated by the development of the technology and input materials. Business forecasts place AM as one of the potentially disruptive technologies, and it is becoming evident that manufacturing and supply chains of the future will be changed due to ongoing developments in the AM technology with different materials and newer applications. Technology selection and material selection are major decisions involved in using AM.