Supplier selection based on a neural network model using genetic algorithm

Authors:
Davood Golmohammadi;Robert C. Creese;Haleh Valian;John Kolassa
Affiliations:
Management Science and Information Systems, University of Massachusetts Boston, Boston, MA;Industrial and Management Systems Engineering Department, West Virginia University, Morgantown, WV;Industrial and Systems Engineering Department, Rutgers-The State University of New Jersey, Piscataway, NJ and JPM Chase, New York, NY;Statistics Department, Rutgers-The State University of New Jersey, Piscataway, NJ
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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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.