Enhancing decision of supplier selection using a genetic algorithm: A case study

Chan, Gaik Yee and Khoh, Chee Tong (2014) Enhancing decision of supplier selection using a genetic algorithm: A case study. In: 2014 10th International Conference on Natural Computation, ICNC 2014. IEEE, pp. 315-320. ISBN 978-1-4799-5150-5

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Abstract

This paper takes a practical case study approach to demonstrate the genetic algorithm (GA)'s ability to help purchasing manager in making a better decision on procurement of products or materials. The GA implemented in the supplier selection function aims to allow purchasing managers to get better decisions in choosing the appropriate suppliers by choosing the appropriate products under various contextual situations. By allowing the purchasing managers to set their criteria based on priority helps the company to choose good product with best price and best quality, thus decreases procurement budget while increases company reputation. Information regarding evaluation criteria and data for our experiments are obtained through a local company which provides automobile service and repairs. Results generated from experiments based on various scenarios by prioritizing different evaluation attributes have demonstrated the GA's ability in choosing the "fittest" solution.

Item Type: Book Section
Additional Information: 2014 10th International Conference on Natural Computation, ICNC 2014; Xiamen; China; 19 August 2014 through 21 August 2014
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 20 Apr 2015 03:40
Last Modified: 20 Apr 2015 03:40
URI: http://shdl.mmu.edu.my/id/eprint/6196

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