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A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization

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  • Luan, Jing
  • Yao, Zhong
  • Zhao, Futao
  • Song, Xin

Abstract

Nowadays, with the development of information technology and economic globalization, supplier selection problem gets more and more attraction. The recent literature shows huge interest in hybrid artificial intelligence (AI)-based models for solving supplier selection problem. In this paper, to solve a multi-criteria supplier selection problem, based on genetic algorithm (GA) and ant colony optimization (ACO), hybrid algorithm of GA and ACO is developed. It combines merits of GA with great global converging rate and ACO with parallelism and effective feedback. A numerical experiment was conducted to optimize parameters and to analyze and compare the performance of the original and hybrid algorithms. Results demonstrate the quality and efficiency improvement of new integrated algorithm, verifying its feasibility and effectiveness. It is an innovative pilot research to leverage hybrid AI-based algorithm of GA and ACO to settle the supplier selection problem, which not only makes a clear methodological contribution for optimization algorithm research, but also can be served as a decision tool and provide management reference for companies.

Suggested Citation

  • Luan, Jing & Yao, Zhong & Zhao, Futao & Song, Xin, 2019. "A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 294-309.
    • Handle: RePEc:eee:matcom:v:156:y:2019:i:c:p:294-309
    DOI: 10.1016/j.matcom.2018.08.011
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    1. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2016. "Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management," International Journal of Production Economics, Elsevier, vol. 174(C), pages 128-141.
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