期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 203, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2022.117460
关键词
Flexible job shop scheduling problem; Machine breakdown; Dynamic scheduling; Imperialist competition algorithm; Convolutional neural network
类别
资金
- National Natural Science Foundation of China [U1904167, 71871204, 51905494]
- Humanities and Social Sciences of Ministry of Education Planning Fund [18YJAZH125]
- Innovative Research Team (in Science and Technology) in University of Henan Province [21IRTSTHN018]
This research proposes an effective two-stage algorithm based on convolutional neural network for solving the flexible job shop scheduling problem. The algorithm is used to train the prediction model and evaluate the robustness of scheduling, with the evaluation done through the proposed RMn metric.
In the actual manufacturing process, the environment of the job shop is complex. There will be many kinds of uncertainties such as random job arrivals, machine breakdowns, order cancellations and other dynamic events. In this paper, an effective two-stage algorithm based on convolutional neural network is proposed to solve the flexible job shop scheduling problem (FJSP) with machine breakdown. A bi-objective dynamic flexible job shop scheduling problem (DFJSP) model with the objective of maximum completion time and robustness is established. In the two-stage algorithm, the first stage is to train the prediction model by convolutional neural network (CNN). The second stage is to predict the robustness of scheduling through the model trained in the first stage. First, an improved imperialist competition algorithm (ICA) is proposed to generate training data. Then, a predictive model constructed by CNN was proposed, and an alternative metric called RMn was developed to evaluate robustness. RMn evaluates that the float time has an effect on the robustness through the information of machine breakdown, workload and float time of the operation. The experimental results show that the proposed two-stage algorithm is effective for solving DFJSP, and RMn can evaluate the robustness of scheduling more quickly, efficiently and accurately.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据