Dynamic grouping scheduling optimization of multiple activity instances in batch processing workflow

Aiming at the shortcomings in existing scheduling methods for batch processing workflow,a dynamic grouping scheduling optimization model for considering the constraints of activity instances on executive capacity demand was established,and an implementation algorithm to solve this problem was presented.In this algorithm,intelligent optimization theory of particle swarm optimization was utilized,and two objective functions such as total residence time of minimum activity instance and total execution costs were optimized,thus a set of Pareto optimal scheduling scheme was produced.The result of simulation experiment showed the effectiveness of this algorithm.