A Variable Granularity Optimization Approach for Task Decomposition

Di Dai, Wanwen Zheng, Yuxiang Sun, Chengcheng Xu, Xianjun Zhu, Xianzhong Zhou

Abstract: In recent years, task decomposition has drawn great attention in the equipment maintenance field. However, many investigations are qualitative, which are hard to execute due to the uneven and irregular resource distribution. To solve this problem, a novel variable granularity method is proposed, which develops a quantitative strategy for a task decomposition issue. First, an initial decomposition is operated based on the maintenance technology and internal structure. Then, three quantitative models are formulated to optimize the task set, which is recursively decomposed until the result satisfies the thresholds of granularity, coupling and equilibrium. Finally, a real experiment is analyzed to validate the effectiveness of the proposed method.