A classifier ensemble for classification of dynamic data. Application to an indoor air quality problem

A classifier ensemble for classification of dynamic data. Application to an indoor air quality problem

M. Lounes Bentaha Alexandre Voisin Pascale Marangé Alexandre Dolgui Olga Battaïa 

Université de Lorraine, CRAN, UMR CNRS 7039, Campus Sciences, BP 70239 54506 Vandoeuvre-lès-Nancy Cedex, France

École des Mines de Nantes, IRCCyN, UMR CNRS 6597, BP 20722 F-44307 Nantes Cedex 3, France

Institut Supérieur de l’Aéronautique et de l’Espace, Toulouse, France

Corresponding Author Email: 
mohand-lounes.bentaha@univ-lorraine.fr, alexandre.voisin@univ-lorraine.fr, pascale.marange@univ-lorraine.fr, alexandre.dolgui@mines-nantes.fr, olga.battaia@isae.fr
Page: 
579-605
|
DOI: 
https://doi.org/10.3166/JESA.49.579-605
Received: 
N/A
| |
Accepted: 
N/A
| | Citation
Abstract: 

This paper adresses the problem of disassembly process planning taking into account the quality or states of the product to be disassembled. We propose an approach which is able to return the best disassembly level for a product considering the disassembly cost and the state of the product and/or the states of its subassemblies or components. The state of the product is represented using the concept of " Potentiel d'Utilisation Résiduel (PUR) " which is assumed to be a Gaussian random variable with known truncated distribution. A stochastic program is proposed to model the problem with the objective of maximizing the disassembly process profit. The latter is calculated as the difference between the positive revenue generated by recovered parts and the costs of the disassembly tasks. The revenue of a recovered part is a function of PUR. The developed approach is tested on two example case studies from the literature to analyze the impact of uncertain product quality on its disassembly process planning.

Keywords: 

disassembly, partial disassembly, product quality, uncertainty

1. Introduction
2. Revue de littérature
3. Définition et formulation du problème
4. Modèle d’optimisation et approche de résolution
5. Illustration numérique
6. Conclusion

Agrawal S., Tiwari M. K. (2006). A collaborative ant colony algorithm to stochastic mixedmodel U–shaped disassembly line balancing and sequencing problem. International Journal of Production Research, vol. 46, no 6, p. 1405–1429.

Altekin F. T., Akkan C. (2012). Task-failure–driven rebalancing of disassembly lines. International Journal of Production Research, vol. 50, no 18, p. 4955–4976.

Aydemir-Karadag A., Turkbey O. (2013). Multi–objective optimization of stochastic disassembly line balancing with station paralleling. Computers & Industrial Engineering, vol. 65, no 3, p. 413–425.

Bentaha M. L. (2015). Combinatorial design of disassembly lines under uncertainty. 4OR, p. 1-2.

Bentaha M. L., Battaïa O., Dolgui A. (2013a). Chance constrained programming model for stochastic profit–oriented disassembly line balancing in the presence of hazardous parts. In IFIP Advances in Information and Communication Technology, vol. 414, p. 103–110. Springer Berlin Heidelberg.

Bentaha M. L., Battaïa O., Dolgui A. (2013b). A cone programming approach for stochastic disassembly line balancing in the presence of hazardous parts. In Proceedings of the 22nd International Conference on Production Research (ICPR 22).

Bentaha M. L., Battaïa O., Dolgui A. (2013c). A decomposition method for stochastic partial disassembly line balancing with profit maximization. In IEEE Automation Science and Engineering (CASE), p. 410–415.

Bentaha M. L., Battaïa O., Dolgui A. (2013d). L-shaped algorithm for stochastic disassembly line balancing problem. In N. Bakhtadze, K. Chernyshov, A. Dolgui, V. Lototsky (Eds.), 7th IFAC Conference on Manufacturing Modelling, Management, and Control, vol. 7, p. 407- 411.

Bentaha M. L., Battaïa O., Dolgui A. (2013e). A stochastic formulation of the disassembly line balancing problem. In IFIP Advances in Information and Communication Technology, vol. 397, p. 397–404. Springer Berlin Heidelberg.

Bentaha M. L., Battaïa O., Dolgui A. (2014a). Disassembly line balancing and sequencing under uncertainty. Procedia CIRP, vol. 15, p. 239-244.

Bentaha M. L., Battaïa O., Dolgui A. (2014b). Disassembly line balancing problem with fixed number of workstations under uncertainty. In 19th IFAC World Congress, p. 3522–3526. Cape Town, South Africa.

Bentaha M. L., Battaïa O., Dolgui A. (2014c). Lagrangian relaxation for stochastic disassembly line balancing problem. Procedia CIRP 2014, vol. 17, p. 56–60.

Bentaha M. L., Battaïa O., Dolgui A. (2015). A bibliographic review of production line design and balancing under uncertainty. IFAC-PapersOnLine, vol. 48, no 3, p. 70-75. (15th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2015)

Bentaha M. L., Battaïa O., Dolgui A. (2015). An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times. International Journal of Production Research, vol. 53, no 6, p. 1807–1818.

Bentaha M. L., Battaïa O., Dolgui A., Hu S. J. (2014). Dealing with uncertainty in disassembly line design. CIRP Annals - Manufacturing Technology, vol. 63, no 1, p. 21 - 24.

Bentaha M. L., Battaïa O., Dolgui A., Hu S. J. (2015). Second order conic approximation for disassembly line design with joint probabilistic constraints. European Journal of Operational Research, vol. 247, no 3, p. 957–967.

Byington C. S., Edwards D., Watson M. (2004). Dynamic signal analysis and neural network modeling for life prediction of flight control actuators. In American Helicopter Society Annual Forum. Baltimore, USA.

Camci F., Valentine G., Navarra K. (2007). Methodologies for integration of phm systems with maintenance data. In IEEE Aerospace Conference Proceedings. Montana, USA.

Commission-Européenne. (2010). Europe 2020: une stratégie pour une croissance intelligente, durable et inclusive. Rapport.

Diez L., Marangé P., Mayer F., Levrat E. (2015). Maintenance as a cornerstone for the application of regeneration paradigm in systems lifecycle. CSD&M’15.

Dong T., Zhang L., Tong R., Dong J. (2006). A hierarchical approach to disassembly sequence planning for mechanical product. The International Journal of Advanced Manufacturing Technology, vol. 30, no 5-6, p. 507-520.

Duflou J., Seliger G., Kara S., Umeda Y., Ometto A.,Willems B. (2008). Efficiency and feasibility of product disassembly: A case-based study. CIRP Annals - Manufacturing Technology, vol. 57, no 2, p. 583-600.

Güngör A., Gupta S. M. (2001). A solution approach to the disassembly line balancing problem in the presence of task failures. International Journal of Production Research, vol. 39, no 7, p. 1427–1467.

Ilgin M., Gupta S. (2012). Remanufacturing modeling and analysis. Boca Raton, FL: CRC Press.

Johnson M. R., Wang M. H. (1995). Planning product disassembly for material recovery opportunities. International Journal of Production Research, vol. 33, no 11, p. 3119–3142.

Kalayci C. B., Gupta S. M. (2013). Ant colony optimization for sequence–dependent disassembly line balancing problem. Journal of Manufacturing Technology Management, vol. 24, no 3, p. 413–427.

Kara S., Pornprasitpol P., Kaebernick H. (2006). Selective Disassembly Sequencing: A Methodology for the Disassembly of End-of-Life Products. CIRP Annals - Manufacturing Technology, vol. 55, no 1, p. 37–40.

Kumar S., Dolev E., Pecht M. (2009). Parameter selection for health monitoring of electronic products. Microelectronics Reliability, vol. 50, p. 161-168.

Lambert A., Gupta S. (2008). Methods for optimum and near optimum disassembly sequencing. International Journal of Production Research, vol. 46, no 11, p. 2845-2865.

Lambert A. J. D. (1999). Linear programming in disassembly/clustering sequence generation. Computers & Industrial Engineering, vol. 36, no 4, p. 723–738.

Lambert A. J. D. (2003). Disassembly sequencing: a survey. International Journal of Production Research, vol. 41, p. 3721–3759.

Lambert A. J. D. (2007). Optimizing disassembly processes subjected to sequencedependent cost. Computers & Operations Research, vol. 34, p. 536-551.

McGovern S. M., Gupta S. M. (2011). The disassembly line, balancing and modeling (2011e éd.). New York, McGraw-Hill Companies.

Peeters J. R., Vanegas P., R. Duflou J., Mizuno T., Fukushige S., Umeda Y. (2013). ffects of boundary conditions on the end-of-life treatment of LCD TVs. CIRP Annals - Manufacturing Technology, vol. 62, no 1, p. 35-38.

Rickli J. L., Camelio J. A. (2014). Partial disassembly sequencing considering acquired end-oflife product age distributions. International Journal of Production Research, vol. 52, no 24, p. 7496-7512.

Riggs R. J., Battaïa O., Hu S. J. (2015). Disassembly line balancing under high variety of end of life states using a joint precedence graph approach. Journal of Manufacturing Systems, In Press.

Santochi M., Dini G., Failli F. (2002). Computer aided disassembly planning: State of the art and perspectives. CIRP Annals - Manufacturing Technology, vol. 51, no 2, p. 507-529.

Shin J. (2009). Decision support methods for closed-loop conceptual design. Thèse de doctorat. Ecole Polytechnique Fédérale de Lausanne.

Subramani A. K., Dewhurst P. (1991). Automatic generation of product disassembly sequences. Annals of the CIRP, vol. 40, no 1, p. 115–118.

Tang Y., Zhou M., Zussman E., Caudill R. (2002). Disassembly modeling, planning, and application. Journal of Manufacturing Systems, vol. 21, no 3, p. 200 - 217. Consulté sur http://www.sciencedirect.com/science/article/pii/S0278612502801625

Tian G., Liu Y., Tian Q., Chu J. (2012). Evaluation model and algorithm of product disassembly process with stochastic feature. Clean Technologies and Environmental Policy, vol. 14, no 2, p. 345–356.

Turowski M., Morgan M. (2005). Disassembly line design with uncertainty. 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 1, p. 954–959.

UN-documents. (1987). Report of the world commission on environment and development: Our common future. Rapport.