The overall framework design of automatic logistics system using a hybrid ANN-PSO model

Abstract

Automated logistic systems are commonly applied in the enterprise logistic processes while allowing to improve the reliability and efficiency of logistics procedures with computer simulation. The logistics system focuses on realizing the space and time efficiency of material and understanding the links of logistics to achieve the optimum economic effect. Therefore, in this study, 150 data were collected from 20 hospitals in Malaysia to adopt the automated logistic system under simulation of the computer to decrease the costs of hospitals and increase the serviceability (output). Since obtaining this information for the automated logistic systems is not easy and has a high cost in a traditional way, this study was performed by the integration of artificial neural network (ANN) and particle swarm optimization (PSO) as the ANN-PSO. The input data that were obtained from the processing of automated logistic system in hospitals verified by using a hybrid of ANN-PSO. Later, the results were measured with five regression indicators of determination coefficient (R2), root mean square (RMSE), Pearson correlation coefficient (r), Nash–Sutcliffe model efficiency coefficient (NSE) and Willmott’s index (WI). Considering the results of RMSE, RSQR and r in both testing and training phases, the good performance of ANN-PSO in determining the effectiveness of applying automated logistic system using computer simulation method in hospital management, in terms of raising its serviceability and reducing the costs was proved. As a result, it was shown that ANN-PSO can successfully determine the effectiveness of using an automated logistic system under computer simulation method in hospital management.