Supplier Selection with Fuzzy TOPSIS

For businesses, carrying out their activities successfully is directly related to the suppliers’ efficiency as well as their own performances. Properly selected suppliers make a significant contribution to the competition capacity of enterprises. Therefore, selecting proper suppliers is regarded as a multiple criteria decision making (MCDM) problem, which includes many qualitative and quantitative factors in the process.In this study, a supplier selection model has been developed in order to help executives with proper supplier selection. In this model, Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method, which is an MCDM method, was used with positive trapezoidal fuzzy numbers. The decision structure and the criteria used in decision support model have been determined based on the literature review. The result of the model demonstrated that all the suppliers evaluated in the study are suitable enough to work with, but the supplier B deserves the first place. Keywords: Fuzzy TOPSIS, Multiple-Criteria Decision Making, Supplier Selection.

A Fuzzy Based Decision Making Approach for Selecting and Evaluating Green Suppliers

In a competitive business environment, green supplier selection approach plays a pivotal role in supply chain management, because, due to growing global concern of environmental protection, green production has become an important factor for almost every manufacturer and will influence the sustainability of a manufacturer in the long run. A performance evaluation system for green suppliers is therefore required to determine the suitability of suppliers to cooperate with the industry. Supplier selection is basically depends on decision makers’ (experts’) assessments. This process inevitably involves various types of uncertainties such as deception, fuzziness and incompleteness due to the shortcomings of the human being’s subjective judgment and it’s variance from one human being to another. However, the existing methods cannot properly integrate uncertainties into the determination of green suppliers and their selection. Nowadays, many companies have begun to implement green supply chain management and to consider environmental issues and the measurement of their suppliers’ environmental performance. Here we have adopted, an effective method for selecting and evaluating green supplier selection; TOPSIS (Technique for order preference by similarity to Ideal Solution

A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony

Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers

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