Open Access

ARTICLE

An Optimal Method for Supply Chain Logistics Management Based on Neural Network

by Abdallah Abdallah¹, Mohammed Dauwed², Ayman A. Aly³, Bassem F. Felemban³, Imran Khan⁴, Bong Jun Choi^5,*

1 School of Engineering Technology, Al Hussein Technical University (HTU), Amman, 11831, Jordan
2 Department of Medical Instrumentation Techniques Engineering, Dijlah University College, Baghdad, Iraq
3 Department of Mechanical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia
4 Department of Electrical Engineering, University of Engineering and Technology, Peshawar, 814, Pakistan
5 School of Computer Science and Engineering, Soongsil University, Seoul, Korea

* Corresponding Author: Bong Jun Choi. Email:

Computers, Materials & Continua 2022, 73(2), 4311-4327. https://doi.org/10.32604/cmc.2022.031514

Received 20 April 2022; Accepted 20 May 2022; Issue published 16 June 2022

Abstract

From raw material storage through final product distribution, a cold supply chain is a technique in which all activities are managed by temperature. The expansion in the number of imported meat and other comparable commodities, as well as exported seafood has boosted the performance of cold chain logistics service providers. On the basis of the standard basic-pursuit (BP) neural network, a rough BP particle swarm optimization (PSO) neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers. To reduce duplicate information in the original data and make the input index more compact, the model employs rough set. Instead of using gradient descent to train the weights of the neural network, particle swarm optimization is utilized to ensure that the output results are not readily caught in local minima and that the network’s generalization capacity is improved. Finally, an example is presented to demonstrate the model’s validity and viability. The findings reveal that the model’s prediction error is 40.94 percent lower than the BP neural network model, and the prediction result is more accurate and dependable, providing a new technique for cold chain food production companies to swiftly pick the best cold chain logistics service provider.

---

Keywords

---