OPTIMIZATION AND SIMULATION OF JOB-SHOP SUPPLY CHAIN SCHEDULING IN MANUFACTURING ENTERPRISES BASED ON PARTICLE SWARM OPTIMIZATION

The production scheduling of supply chain is the key to the improvement of production efficiency and resource utilization in manufacturing enterprise. For effective scheduling of job-shop production, this paper puts forward a scheduling optimization method for job-shop supply chain based on particle swarm optimization (PSO), and proves that the PSO is feasible and valid to solve production scheduling problems through example analysis. In addition, a simulation system was established based on the intelligent algorithm and Microsoft SQL Server Platform to solve production scheduling problems. The research shows that the PSO can effectively overcome the nonconvergence problem in production scheduling, and rapidly obtain the optimal solution for job-shop scheduling; the PSO outperforms a common genetic algorithm in the convergence to the optimal solution. The research findings provide a valuable theoretical reference for manufacturing enterprises to solve and optimize production scheduling problems.