A Framework for Supplier Selection Problem using a Hierarchical Fuzzy TOPSIS and Genetic Algorithm

D. Mirheydari (Iran)


Supply Chain Management, Fuzzy TOPSIS, Genetic Algorithm, primary evaluation, Supplier selection.


This article is aimed to develop a Hybrid approach for supplier selection and order allocation process in a multisource, multi-period situation and by considering quantity discount and supplier’s capacity limitation.
First of all, all the needed materials used by the organization were classified with respect to their nature deploying ABC method.

Then primary evaluation of all suppliers was applied to the organization in order to identify potential and qualified suppliers which have minimum requirements of organization. For the sake of that, a check list including appropriate criteria has been designed.

Afterwards, considering a specific material to order, appropriate criteria were determined, taking into account the particular material’s specification as well as the organization’s needs.

A fuzzy hierarchical TOPSIS was used to rank potential suppliers in terms of these criteria and determine which suppliers are the best. Finally, we utilize genetic algorithm to conclude what quantities are optimum to be allocated to selected suppliers and in which periods, to reach the minimum total cost (purchasing costs, transaction costs and holding cost) over the planning horizon. This model was applies to Mobarakeh Steel Company (MSC). To validate the results obtained, they were compared with the results received by using simulated annealing techniques. The results show that this approach is effective, robust and easy to apply.

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