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Erschienen in: Neural Computing and Applications 1/2013

01.12.2013 | Original Article

A new mixed fuzzy-LP method for selecting the best supplier using fuzzy group decision making

verfasst von: Abbas Sepehriar, Reza Eslamipoor, Arash Nobari

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

Even though there have been so many articles and studies that focused on the supplier selection problem, a few of them considered the major decisions related to supplier selection. In this paper, the supplier selection problem is studied under a novel fuzzy approach in multiple sourcing conditions to select appropriate supplier in group decision-making environment regarding multiple sources. The use of the model is suitable in evaluating situations that involve subjectivity, vagueness and imprecise information. The group data have been used in multiple criteria decision making with multiple objective supplier selection. Trapezoidal fuzzy numbers are applied to evaluate the weights of supplier selection criteria under uncertainty conditions. The use of the model is suitable in evaluating situations that involve subjectivity, vagueness and imprecise information. The proposed method can use imperfect or insufficient knowledge of data to deal with decision-making problems. Additionally, fuzzy linear programming concept developed to enhance the optimum amount of the purchase under surrounding circumstance. This model allows the decision maker to meet the challenges with different weight and criteria for each criterion with respect to the required strategy. The considered fuzzy method leads to more consistence results compared to the existing methods and shows its preference in a numerical example.

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Metadaten
Titel
A new mixed fuzzy-LP method for selecting the best supplier using fuzzy group decision making
verfasst von
Abbas Sepehriar
Reza Eslamipoor
Arash Nobari
Publikationsdatum
01.12.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1458-z

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