A Combination of Analytic Hierarchy Process Method and Machine Learning for Supplier Selection in Supply Chain Management
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8472-5_5
Reference16 articles.
1. Baryannis, G., Dani, S., Validi, S., Antoniou, G.: Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management, in Revisiting Supply Chain Risk. Springer Series in Supply Chain Management, pp. 53–71. Springer (2019)
2. Chai, J., Ngai,W.T.: Decision-making techniques in supplier selection: recent accomplishments and what lies ahead. Expert Syst. Appl. 140 (2020)
3. Constante-Nicolalde, F.-V., Guerra-Terán, P., Pérez-Medina, J.-L.: Fraud prediction in smart supply chains using machine learning techniques. In: Applied Technologies, vol. 1194, pp. 145–159. Springer (2020)
4. Hasan, K., Asil, O., Angappa, G., Ferhan, C.: An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Comput. Ind. Eng. 101(C), 599–613 (2016)
5. Ho, W., Xu, X., Dey, P.K.: Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur. J. Oper. Res. 202(1), 16–24 (2010)
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