Abstract
Disassembly processes of used manufactured products are subject to uncertainties. The optimal disassembly level that minimizes the costs of these processes and maximizes the end of life components values is hard to establish. In this work, we propose a method to find influences and causalities between the main disassembly performance indicators in order to decide the optimal disassembly policy. The proposed model highlights the temporal dependencies between variables of the system and is validated using the Bayesia Lab software. In the final part of the chapter, the results of method implementation on a reference case study are presented in order to demonstrate the performance of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pearce, I.A.: The profit making allure of product reconstruction. MIT Sloan Management Review 50(3), 59–63 (2009)
Lambert, A.J.D., Gupta, S.M.: Disassembly Modeling for Assembly, Maintenance, Reuse, and Recycling. CRC Press, Boca Raton (2005)
Geiger, D., Zussmann, E.: Probabilistic Reactive Disasssembly Planning. CIRP Annals 45(1), 49–52 (1996)
Grochowski, D.E.: Parameter Estimation for Optimal Disassembly Planning, pp. 2490–2496. IEEE (2007)
Bayındıra, Z.P., Dekkerb, R., Porrasb, E.: Determination of recovery effort for a probabilistic recovery system under various inventory control policies. The International Journal of Management Science 34, 571–584 (2006)
Jensen, F.V.: Bayesien Networks and Decision Graphs. Springer, Heidelberg (2001)
Clemen, R., Reilly, T.: Making hard decisions with Decision Tools. Duxbury Thomson Learning (2001)
Godichaud, M.: Outils d’aide à la décision pour la sélection des filières de revalorisation des produits issus de la déconstruction des systèmes en fin de vie, thèse de doctorat, Université de Toulouse (2010)
Alami, M.: Lot économique de pièces de rechange à produire en tenant compte des différentes phases du cycle de vie du produit, Thèse de doctorat, Université Laval, Québec (2009)
Conrady, S.: Introduction to Bayesian Networks Conrady Applied Science, LLC - Bayesia’s North American Partner for Sales and Consulting (2011)
Hu, J., Zhang, L., Ma, L., Liang, W.: An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm. Expert Systems with Applications 38, 1431–1446 (2011)
Portinale, L., Raiteri, D.C., Montani, S.: Supporting reliability engineers in exploiting the power of Dynamic Bayesian Networks. International Journal of Approximate Reasoning 51, 79–195 (2010)
Duta, L., Caciula, I., Addouche, S.: On the profitability of the disassembly processes. In: Proceedings of the 18th IFAC World Congress, Milan (2011)
Imtanavanich, P., Gupta, S.M.: Generating a Disassembly-to-Order Plan. In: Proceedings of the 2007 Northeast Decision Science, on CD-ROM (2007)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning, Thesis University of California (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Duta, L., Douche, S.A. (2012). Dynamic Bayesian Network for Decision Aided Disassembly Planning. In: Borangiu, T., Thomas, A., Trentesaux, D. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing Control. Studies in Computational Intelligence, vol 402. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27449-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-27449-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27448-0
Online ISBN: 978-3-642-27449-7
eBook Packages: EngineeringEngineering (R0)