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Research on Multi-object Job-shop Scheduling Strategy under Uncertain Environment

机译:不确定环境下的多目标作业车间调度策略研究

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In this paper, we propose an improved PSO algorithm to optimize the RBF neural network(IPSO-RBF)for function approximation on the basis of the multi-objective stochastic scheduling model established by predecessors using stochastic programming knowledge. The experimental results show that the improved algorithm can solve the job-shop scheduling problem. The accuracy and speed of the solution have been improved.
机译:本文基于前人利用随机规划知识建立的多目标随机调度模型,提出了一种改进的PSO算法对RBF神经网络(IPSO-RBF)进行函数逼近优化。实验结果表明,改进算法可以解决车间作业调度问题。解决方案的准确性和速度得到了提高。

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