Supplier selection based on a neural network model using genetic algorithm.

Full Abstract

In this paper, a decision-making model was developed to select suppliers using neural networks (NNs). This model used historical supplier performance data for selection of vendor suppliers. Input and output were designed in a unique manner for training purposes. The managers' judgments about suppliers were simulated by using a pairwise comparisons matrix for output estimation in the NN. To obtain the benefit of a search technique for model structure and training, genetic algorithm (GA) was applied for the initial weights and architecture of the network. The suppliers' database information (input) can be updated over time to change the suppliers' score estimation based on their performance. The case study illustrated shows how the model can be applied for suppliers' selection.

Author information

Author/s: Golmohammadi, Davood (D); Creese, Robert C (RC); Valian, Haleh (H); Kolassa, John (J);

Affiliation: Management Science and Information Systems,University of Massachusetts Boston, Boston, MA 02125, USA. davood.golmohammadi(-atsign-)umb.edu

Journal and publication information

Publication Type: Journal Article; Validation Studies

Journal: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council (IEEE Trans Neural Netw), published in United States. (Language: eng)

Reference: 2009-Sep; vol 20 (issue 9) : pp 1504-19

Dates: Created 2009/09/03; Completed 2009/11/04;

PMID: 19695996, status: MEDLINE (last retrieved date: 11/4/2009)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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