Abstract: A supply chain that is effective and of the highest caliber boosts customer happiness as well as sales and earnings, increasing the company's competitiveness in the market. It has been discovered that the standard supply chain management technique leaves the supply chain with weak supply chain stability because it has a low ability to withstand the manufacturer's production behaviour. An enterprise supply chain resistance management model is built using the study's proposed particle swarm optimisation technique, which is based on a genetic algorithm with stochastic neighbourhood structure, to solve this issue. The suggested technique outperformed the other two algorithms utilised for comparison in a performance comparison test, with a stable particle swarm fitness value of 0.016 after 800 iterative iterations and the fastest convergence. The proposed model was then empirically examined, and the results revealed that the production team using the model completed the same volume of orders in 32 days while making $460,000 more in profit. With scores of 4.5, 4.5, 4.3, 4.3, 4.2, and 4.2, respectively, the team also had the lowest values of the six forms of employee anti-production conduct, outperforming the comparative management style. In summary, the study proposes an anti-disturbance management model for enterprise supply chains that can rationalise the scheduling of manufacturers' production behaviour and thus improve the stability of the supply chain.

Tongqing Dai, “Research on Enterprise Supply Chain Anti-Disturbance Management Based on Improved Particle Swarm Optimization Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01408102

@article{Dai2023,
title = {Research on Enterprise Supply Chain Anti-Disturbance Management Based on Improved Particle Swarm Optimization Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01408102},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01408102},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Tongqing Dai}
}