Name
Abstract | ||
|---|---|---|
•Predicting remanufactured product demand is a highly, non-linear problem.•Ensemble machine learning algorithm outperforms other prediction models.•Consumer-generated predictors are more important than seller-generated predictors.•Variable importance ranking and partial dependency plots enhance comprehensibility. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ejor.2019.08.015 | European Journal of Operational Research |
Keywords | DocType | Volume |
Data mining,Remanufactured products,Machine learning,Regression trees | Journal | 281 |
Issue | ISSN | Citations |
3 | 0377-2217 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Truong Van Nguyen | 1 | 1 | 0.36 |
| Li Zhou | 2 | 20 | 3.58 |
| Alain Yee-loong Chong | 3 | 635 | 41.95 |
| Boying Li | 4 | 36 | 9.80 |
| Xiaodie Pu | 5 | 1 | 0.36 |