A CLOUD TOPSIS MODEL FOR GREEN SUPPLIER SELECTION

Due to stringent governmental regulations and increasing consciousness of the customers, the present day manufacturing organizations are continuously striving to engage green suppliers in their supply chain management systems. Selection of the most efficient green supplier is now not only dependant on the conventional evaluation criteria but it also includes various other sustainable parameters. This selection process has already been identified as a typical multi-criteria group decision-making task involving subjective judgments of different participating experts. In this paper, a green supplier selection problem for an automobile industry is solved while integrating the Cloud model with the technique for order of preference by similarity to an ideal solution (TOPSIS). The adopted method is capable of dealing with both fuzziness and randomness present in the human cognition process while appraising performance of the alternative green suppliers with respect to various evaluation criteria. This model identifies green supplier S4 as the best choice. The derived ranking results using the adopted model closely match with those obtained from other variants of the TOPSIS method. The Cloud model can efficiently take into account both fuzziness and randomness in a qualitative attribute, and effectively reconstruct the qualitative attribute into the corresponding quantitative score for effective evaluation and appraisal of the considered green suppliers. Comparison of the derived ranking results with other MCDM techniques proves applicability, potentiality and solution accuracy of the Cloud TOPSIS model for the green supplier selection

Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

As enterprises pay more and more attention to environmental issues, the green supply chain management (GSCM) mode has been extensively utilized to guarantee profit and sustainable development. Greensupplierselection(GSS),whichisakeysegmentofGSCM,hasbeeninvestigated to put forward plenty of GSS approaches

Pembinaan indeks jenayah curi kenderaan dengan pendekatan berbilang kriterium dalam persekitaran kabur

An index is a measure of the performance of a situation that displays the value of the final score resulting from a combination of several criteria values mathematically. The existing index of vehicle theft is built on the assumption that all criteria are equally important and based on numerical data only without considering ambiguity aspect, particularly the causes of the crime. Moreover, there was no decision-makers involvement to assess the level of contribution of criminal criteria based on their knowledge and experience. Therefore, this study developed an improved InJeCK by considering the fuzziness. InJeCK also involves decision-makers in determining the degree of importance of vehicle theft crime criteria, in addition to numerical data analysis obtained from related agencies. The subjective weights in this study utilized Z-numbers through the level of contribution of the criteria evaluated in the representation of two triangular fuzzy numbers. Objective weights used the degree of uncertainty through entropy measurements. These two weights were aggregated to form an aggregated weight that balances the weaknesses of both subjective and objective weights. Next, InJeCK was computed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank 82 areas in Peninsular Malaysia. InJeCK values were visualized using color maps to be more informative. The results show that the criteria of car theft, higher education and unemployment are the three most influential criteria in vehicle theft crime based on the aggregated weights. The InJeCK values show that Kuala Lumpur is the riskiest area for vehicle theft cases. This study has contributed to the field of multi-criteria decision-making by considering fuzzy environment in the development of vehicle theft crime index. Besides, the findings of the study can also assist those involved in curbing vehicle theft in particular and property crime in general

Evaluation of Supllier Risk Using Fuzzy Logic

Diplomová práce se zabývá vyhodnocením dodavatelského rizika za využití principů fuzzy logiky pro vybranou společnost vinařství Soška. Tato společnost je, stejně jako mnoho jiných, nucena přejít na prodej přes internet z důvodu zavedení vládních restrikcí v době pandemie. Pro úspěšný internetový prodej je potřeba vybrat optimální e-shopový software pro společnost na základě jejích stanovených požadavků. Za tímto účelem jsou vytvořeny 2 fuzzy modely v programech Microsoft Excel a MATLAB, které si kladou za cíl vyhodnotit riziko při výběru dodavatele. Na základě tohoto hodnocení jsou zformulovány výstupy a doporučení pro výběr optimální varianty pro společnost. V úvodu práce jsou představeny základní teoretické poznatky, které jsou využity v praktické části práce.This diploma thesis deals with the evaluation of supplier risk for a chosen company Vinařství Soška using the fuzzy logic principles. This company like many others is forced to shift its sales to the Internet environment due to the government restrictions established to reflect the pandemic situation. For a successful Internet business, it is necessary to choose an optimal ecommerce software based on the company requirements. For this purpose, two fuzzy logic models are built in Microsoft Excel and MATLAB software. Their aim is to evaluate the potential risk of all of the suppliers. Based on this evaluation, the results are transformed into specific recommendations on how to choose the optimal solution for the company. The first chapters deal with the general theory that is later used in the analytical part.

Supplier evaluation and selection in fuzzy environments: a review of MADM approaches

In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described by fuzzy sets. This study provides a review of applications of MADM approaches for evaluation and selection of suppliers in a fuzzy environment. To this aim, a total of 339 publications were examined, including papers in peer-reviewed journals and reputable conferences and also some book chapters over the period of 2001 to 2016. These publications were extracted from many online databases and classified in some categories and subcategories according to the MADM approaches, and then they were analysed based on the frequency of approaches, number of citations, year of publication, country of origin and publishing journals. The results of this study show that the AHP and TOPSIS methods are the most popular approaches. Moreover, China and Taiwan are the top countries in terms of number of publications and number of citations, respectively. The top three journals with highest number of publications were: Expert Systems with Applications, International Journal of Production Research and The International Journal of Advanced Manufacturing Technology