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

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

Applied Mathematics and Computation, 2015, vol. 250, issue C, 958-972

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.

Keywords: Supplier selection; Order quantity allocation; TD-FP-growth algorithm; Optimization artificial immune network; Particle swarm optimization (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:250:y:2015:i:c:p:958-972

DOI: 10.1016/j.amc.2014.11.015

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