Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/155486 
Erscheinungsjahr: 
2013
Quellenangabe: 
[Journal:] Journal of Remanufacturing [ISSN:] 2210-4690 [Volume:] 3 [Publisher:] Springer [Place:] Heidelberg [Year:] 2013 [Pages:] 1-17
Verlag: 
Springer, Heidelberg
Zusammenfassung: 
China's recycling roadmap and technology scheme for used automotive electronic control components are investigated. The mathematical analysis model of the remanufacturing process is established on the basis of stochastic network technology, as well as on the graphical evaluation and review technique (GERT). In addition, the calculation method used for estimating single-product remanufacturing time is examined. The objective of this study is to determine the probability of success for the remanufacturing of used automotive electronic control components and remanufacturing time. On the basis of experimental parameters, we simulate the remanufacturing process using the Monte Carlo simulation in Crystal Ball. Compared with the result of the GERT model (8.5114 h), the simulation error rate is 0.225%. This consistency in results indicates that both the stochastic network model and Crystal Ball can accurately simulate the remanufacturing process of used automotive electronic control components, making these techniques feasible approaches for such processes. Aside from numerical experiments on and sensitivity analyses of key processes, the relationship between total remanufacturing time and five influencing factors is identified. Total remanufacturing time can be significantly reduced by optimizing the key processes. The optimization methods are also investigated.
Schlagwörter: 
Used automotive electronic control components
Recycling
Stochastic network model
Monte Carlo simulation in Crystal Ball
Simulation
China
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Erscheint in der Sammlung:

Datei(en):
Datei
Größe
3.25 MB





Publikationen in EconStor sind urheberrechtlich geschützt.