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

• Article •

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

CHEN Zhi-gang, WEN Yi-ping, KANG Guo-sheng

1.School of Information Science and Engineering, Central South University, Changsha 410083, China；2.Key Laboratory of Knowledge Processing and Networked Manufacture, Hunan University of Science and Technology, Xiangtan 411201, China

Online:2012-08-15 Published:2012-08-25

成批处理工作流动态分组调度优化方法

陈志刚，文一凭，康国胜

1.中南大学信息科学与工程学院,湖南长沙4100831；2.湖南科技大学知识处理与网络化制造湖南省普通高校重点实验室,湖南湘潭411201

Abstract

Abstract: 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.

Key words: batch processing workflow, scheduling, multi-objective, constraints, particle swarm

摘要： 针对现有成批处理工作流调度方法的不足，建立考虑活动实例对执行者执行能力需求等约束的动态分组调度优化模型，提出一种解决该问题的实现算法。算法主要思想是利用微粒群算法的智能优化原理，同时优化最小化活动实例的停留时间总和与执行开销总和这两个目标函数，最终产生一组满足约束条件的Pareto优化调度方案。仿真实验说明了算法的有效性。

关键词: 成批处理工作流, 调度, 多目标, 约束, 微粒群

CLC Number:

TP311

CHEN Zhi-gang, WEN Yi-ping,, KANG Guo-sheng. Dynamic grouping scheduling optimization of multiple activity instances in batch processing workflow[J]. .

陈志刚，文一凭,，康国胜. 成批处理工作流动态分组调度优化方法[J]. .

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