An artificial intelligence model based on LS-SVM for third-party logistics provider selection
الموضوعات : مجله بین المللی ریاضیات صنعتیB. Vahdani 1 , Sh. Sadigh ‎Behzadi‎ 2 , S. M. ‎Mousavi‎ 3
1 - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
2 - Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran.
3 - Industrial Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran.
الکلمات المفتاحية: Artificial Intelligence (AI), Least squares support vector m, Cross validation, Third-party logistics (3PL) pr,
ملخص المقالة :
The use of third-party logistics (3PL) providers is regarded as new strategy in logistics management. The relationships by considering 3PL are sometimes more complicated than any classical logistics supplier relationships. These relationships have taken into account as a well-known way to highlight organizations' flexibilities to regard rapidly uncertain market conditions, follow core competencies, and provide long-term growth strategies. Choosing service providers has been considered as a notable research area in the last two decades. The review of the literature represents that neural networks have proposed better performance than traditional methods in this area. Therefore, in this paper, a new enhanced artificial intelligence (AI) approach is taken into consideration to assist the decision making for the logistics management which can be successfully presented in cosmetics industry for long-term prediction of the real performance data. The presented AI approach is based on modern hybrid neural networks to improve the decision making for the 3PL selection. The model can predict the overall performance of the 3PL according to least squares support vector machine and cross validation technique. In addition, the proposed AI approach is given for the 3PL selection in a real case study for the cosmetics industry. The computational results indicate that the proposed AI approach provides high performance and accuracy through the real-life situations prediction along with comparing two other two well-known AI methods.
