Research on Supply Chain Optimisation Management Method Integrating Employee Behaviour Factors by Improving PSO Algorithm

Wenhui Li and Can Wang
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Wenhui Li: School of Transportation Management, Zhengzhou Railway, Vocational and Technical College, Zhengzhou 451460, P. R. China
Can Wang: ��School of Artificial Intelligence, Zhengzhou Railway Vocational and Technical College, Zhengzhou 451460, P. R. China

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 01, 1-18

Abstract: With the continuous advancement of the trend of economic globalisation and the in-depth development of personalised services, the manufacturing mode has begun to change to service-oriented manufacturing, and the focus of enterprises has gradually shifted from the industrial chain to the supply chain. However, at present, accidents often occur in the supply chain around products, such as changes in orders, lack of resources in a short period of time, etc. These interference events are difficult to control and cause great damage to the normal operation and economic interests of enterprises for a long time. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. The study uses system dynamics to analyses employee counterproductive behaviour, develops a disturbance management model incorporating employee behavioural factors, and solves it with an improved particle swarm optimisation (PSO) algorithm. The experimental results show that the maximum number of noninferior solutions obtained by the improved PSO algorithm is 14 and 12, respectively. Compared with the GA_TOM (Genetic Algorithm_TOM), the improved algorithm is closer to the ideal pareto front. In the MS index, the average and minimum values obtained by the improved PSO algorithm are 0.57 and 0.609, respectively, which can cover more ideal pareto fronts. It shows that the algorithm effectively improves the stability and security of the supply chain, and provides a practical reference for the supply chain optimisation of manufacturing enterprises.

Keywords: Anti-production behaviour; particle swarm optimisation algorithm; supply chain; interference management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500059

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