Abstract
Batch task scheduling in cloud manufacturing has dynamic, real-time characteristic and the presence of big data concurrency and exchange requirements, while traditional workshop tasks scheduling models and algorithms can’t fit. In order to effectively save the time and reduce the cost of workshop production, an optimization model is put forward at first. And then improved cooperative particle swarm optimization algorithm with fast convergence and strong ability to avoid local optimization is used to solve the tasks scheduling problems. At last simulation experiment analysis results prove its effectiveness. (Received, processed and accepted by the Chinese Representative Office.)
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