Application of multi-criteria decision-making theory with VIKOR-WASPAS-Entropy methods: A case study of silent Genset
Materials Today: Proceedings, ISSN: 2214-7853, Vol: 50, Page: 2416-2423
2022
- 41Citations
- 43Captures
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Article Description
Due to incredibly challenging social, economic, technical, and environmental aspects, decision-making must evaluate several conflicting agendas. Therefore, rule-based decision built on a single criterion is no longer capable of dealing with these issues. Generators, especially silent ones, are essential for industries, hospitals, schools, colleges and hotels. However, silent Genset (ŚĠ) of different companies and capacities exist in the market with various attributes/criteria such as power consumption, fuel tank capacity, fuel consumption etc. So, it becomes difficult for the firms/purchaser/wholesaler/retailer to pick the most appropriate ŚĠ. So, in this present work, multi-criteria decision making (MCDM) methodologies VIKOR (Vlsekriterijumsko kompromisno Rongiranje) and WASPAS (Weighted Aggregated Sum Product Assessment) are utilized to rank the different alternatives available in the market. First, the weights of importance to ŚĠ sets are applied identically and with objective weights. Then, the objective weights are assigned with the entropy weight method (ĖŴΜ) without the decision maker’s choice. Finally, the ranks obtained with VIKOR and WASPAS are displayed. Furthermore, more alternatives and criteria can be considered with varying MCDM techniques of weighting and ranking. As a result, for industrial engineers who play a crucial part in decision-making, the provided approach for Genset selection is convenient, practical, and productive.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2214785321067663; http://dx.doi.org/10.1016/j.matpr.2021.10.259; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85124951167&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2214785321067663; https://dx.doi.org/10.1016/j.matpr.2021.10.259
Elsevier BV
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