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The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation

Author

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  • Kuo, R.J.
  • Pai, C.M.
  • Lin, R.H.
  • Chu, H.C.

Abstract

This study firstly uses one of the association rule mining techniques, a TD-FP-growth algorithm, to select the important suppliers from the existing suppliers and determine the importance of each supplier. A hybrid artificial immune network (Opt-aiNet) and particle swarm optimization (PSO) (aiNet-PSO) is then proposed to allocate the order quantity for the key suppliers at minimum cost. In order to verify the proposed method, a case company’s daily purchasing ledger is used, with emphasis on the consumer electronic product manufacturers. The computational results indicate that the TD-FP-growth algorithm can select the key suppliers using the historical data. The proposed hybrid method also provides a cheaper solution than a genetic algorithm, particle swam optimization, or an artificial immune system.

Suggested Citation

  • Kuo, R.J. & Pai, C.M. & Lin, R.H. & Chu, H.C., 2015. "The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 958-972.
    • Handle: RePEc:eee:apmaco:v:250:y:2015:i:c:p:958-972
    DOI: 10.1016/j.amc.2014.11.015
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    References listed on IDEAS

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    2. Yaqiong Lv & Shangjia Xiang & Tianyi Zhu & Shuzhu Zhang, 2020. "Data-Driven Design and Optimization for Smart Logistics Parks: Towards the Sustainable Development of the Steel Industry," Sustainability, MDPI, vol. 12(17), pages 1-12, August.
    3. Agapito, Giuseppe & Guzzi, Pietro Hiram & Cannataro, Mario, 2019. "Parallel extraction of association rules from genomics data," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 434-446.

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