Havaeji, H.; Dao, T.-M.; Wong, T. Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand. Mathematics2023, 11, 4669.
Havaeji, H.; Dao, T.-M.; Wong, T. Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand. Mathematics 2023, 11, 4669.
Havaeji, H.; Dao, T.-M.; Wong, T. Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand. Mathematics2023, 11, 4669.
Havaeji, H.; Dao, T.-M.; Wong, T. Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand. Mathematics 2023, 11, 4669.
Abstract
This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand fluctuations. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its own importance, and we applied the Feature Weighting approach to analyse their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a stronger influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
