Name
Abstract | ||
|---|---|---|
The fast and accurate estimation of makespan is essential for the determination of the delivery date and the sustainable development of the enterprise. In this paper, a high-quality training dataset is constructed and an adaptive ensemble model is proposed to achieve fast and accurate makespan estimation. First, both the logistics features extracted by the Pearson correlation coefficient and the new meaningful nonlinear combination features dug out by gene expression programming are first involved in this paper for constructing a high-quality dataset. Secondly, an improved clustering with elbow criterion and a resampling operation are applied simultaneously to generate representative subsets; and correspondingly, several back propagation neural network (BPNN) with the architecture optimized by genetic algorithm are trained by these subsets respectively to generate effective diverse learners; and then, a K-nearest neighbor based dynamic weight combination strategy which is sensitive to current testing sample is proposed to make full use of the learner's positive effects and avoid its negative effects. Finally, the results of effective experiments prove that both the newly involved features and the improvements in the proposed ensemble are effective. In addition, comparison experiments confirm that the proposed enhanced ensemble of BPNNs outperforms significantly the prevailing approaches, including single, ensemble and hybrid models. And hence, the proposed model can be utilized as a convenient and reliable tool to support customer order acceptance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10845-020-01585-y | JOURNAL OF INTELLIGENT MANUFACTURING |
Keywords | DocType | Volume |
Makespan estimation, Ensemble of BPNN, Gene expression programming, Clustering, Genetic algorithm | Journal | 32 |
Issue | ISSN | Citations |
2 | 0956-5515 | 1 |
PageRank | References | Authors |
0.37 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lixin Cheng | 1 | 1 | 0.37 |
| Qiuhua Tang | 2 | 1 | 0.37 |
| Zikai Zhang | 3 | 8 | 5.89 |
| Shiqian Wu | 4 | 1347 | 85.75 |