Remanufacturability Evaluation Method for Used Vehicles Based on Stacking Ensemble Learning Framework

This paper proposes a novel method for evaluating the remanufacturability of used vehicles based on Stacking-Based Ensemble Learning Algorithm (SBELA). A method that combines a supply chain evolutionary game model is proposed to construct a Hybrid Dataset (HD), which aims to deal with Remanufacturin...

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Veröffentlicht in:	IEEE access 2023, Vol.11, p.135922-135933
Hauptverfasser:	Wang, Qiucheng, Sun, Weice, Liu, Zhengqing
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Costs Data models Ensemble learning evaluation of remanufacturability Games hybrid dataset Machine learning Manufacturing processes Performance evaluation Predictive models Production management Regression Remanufacturing remanufacturing data gap Solid modeling Stacking Stacking ensemble learning Supply chains Used automobiles Vehicles
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