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首页> 外文期刊>International Journal of Simulation Modelling >OPTIMIZATION AND SIMULATION OF JOB-SHOP SUPPLY CHAIN SCHEDULING IN MANUFACTURING ENTERPRISES BASED ON PARTICLE SWARM OPTIMIZATION
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OPTIMIZATION AND SIMULATION OF JOB-SHOP SUPPLY CHAIN SCHEDULING IN MANUFACTURING ENTERPRISES BASED ON PARTICLE SWARM OPTIMIZATION

机译：基于粒子群优化的制造企业工作店供应链调度的优化与仿真

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摘要

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.
机译：供应链的生产调度是制造企业生产效率和资源利用的关键。为了有效调度工作店生产，本文提出了基于粒子群优化（PSO）的作业店供应链调度优化方法，并证明PSO是可行的，通过示例分析解决生产调度问题。此外，基于智能算法和Microsoft SQL Server平台建立了仿真系统，以解决生产调度问题。该研究表明，PSO可以有效地克服生产调度中的非反感问题，并迅速获得工作店调度的最佳解决方案; PSO在收敛到最佳解决方案的常见遗传算法中优于常见的遗传算法。研究结果为制造企业解决和优化生产调度问题提供了有价值的理论参考。

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