Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-28T04:50:33.217Z Has data issue: false hasContentIssue false
  • English
  • Français

Quality requirements allocation method based on industrialdata

Published online by Cambridge University Press:  22 May 2014

A. Gay ,
R. Toscano ,
A. Poncet  and
P. Lyonnet
A. Gay
Affiliation:
Universitéde Lyon, Laboratoire de Diagnostic et Imagerie des Procédés Industriels EA 3719 ENISE, 74 rue des Acieries, 42000, Saint-Etienne, France
R. Toscano*
Affiliation:
Volvo Group Truck Technology-Power Train Engineering, 150 rue Edouard Herriot, ZAC Vallée Ozon, 69970 Chaponnay, France
A. Poncet
Affiliation:
Université de Lyon, Laboratoire de Tribologie et de Dynamique des Systèmes CNRS UMR 5513 ECL/ENISE, 58 rue Jean Parot, 42023 Saint-Etienne Cedex 2, France
P. Lyonnet
Affiliation:
Université de Lyon, Laboratoire de Tribologie et de Dynamique des Systèmes CNRS UMR 5513 ECL/ENISE, 58 rue Jean Parot, 42023 Saint-Etienne Cedex 2, France
*
a Corresponding author:[email protected]
Get access

Abstract

Today, to cope with the complexity of the global organization, the industrial companyneeds to be more structured. New processes have to be developed due to more and moreambitious quality requirements. A new problematic arises: what is needed to offer to allcustomers a product that meets the quality requirements of a local market? The mainobjective of this paper is to propose a quality requirements allocation method thatmatches the market specifications and the customer satisfaction. This is in contrast withthe traditional allocation methods which are often time-consuming to implement or do notfocus on the customer satisfaction for the definition of the quality targets. The proposedmethod is inspired from reliability allocation method and is formulated as a feasibilityproblem. In this context the notion of optimality of the solution is not being sought, theobjective is “only” to find out a solution that satisfies the global target quality. Thisallows determining some local quality targets in accordance with industrial data.

Keywords

Quality allocationfault frequencyproduct life cyclefield experienceautomotive vehicle
Type
Research Article
Information
Mechanics & Industry , Volume 15 , Issue 3 , 2014 , pp. 207 - 216
Copyright
© AFM, EDP Sciences 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

F. Bicking, Allocation de la Fiabilité par algorithme génétique : application à la conception d’un Système Instrumenté de Sécurité, Journal national de la recherche en IUT (2009) 30–52
Chang, Y.C., Chang, K.H., Liaw, C.S., Innovative reliability allocation using the maximal entropy ordered weighted averaging method, Comput. Ind. Eng. 57 (2009) 1274–1281 CrossRefGoogle Scholar
M. Grant, S. Boyd, CVX: Matlab Software for Disciplined Convex Programming, version 1.21. http://cvxr.com/cvx, 2010
Hsieh, C.C., Lin, M.H., Reliability-oriented multi-resource allocation in a stochastic-flow network, Reliab. Eng. Syst. Safety 81 (2003) 155–161 CrossRefGoogle Scholar
E.T. Jaynes, Probability Theory – The Logic of Science, Cambridge University Press, 2006
W. Kuo, R. Wan, Recent Advances in Optimal Reliability Allocation, in : Computational Intelligence in Reliability Engineering: Evolutionary Techniques in Reliability Analysis and Optimization, G. Levitin Ed. Series Studies in Computational Intelligence, Springer, 2007, Vol. 39
Liaw, C.S., Chang, Y.C., Chang, K.H., Chang, T.Y., ME-OWA based DEMATEL reliability apportionment method, Expert Systems with Applications 38 (2011) 9713–9723 CrossRefGoogle Scholar
Lisnianski, A., Extended block diagram method for a multi-state system reliability assessment, Reliab. Eng. Syst. Saf. 92 1601–1607 (2007) CrossRefGoogle Scholar
P. Lyonnet, Ingénierie de la fiabilité, Lavoisier Tec Doc, 2006
Ouzineb, M., Nourelfath, M., Gendreau, M., Tabu search for the redundancy allocation problem of homogenous series-parallel multi-state systems, Reliab. Eng. Syst. Saf. 93 (2008) 1257–1272 CrossRefGoogle Scholar
Lambda predict Users Guide, ReliaSoft Corporation AZ ReliaSoft Publishing, 2007
Tian, Z., Levitin, G., Zuo, M.J.. A joint reliability-redundancy optimization approach for multi-state series-parallel systems, Reliab. Eng. Syst. Safe. 94 (2009) 1568–1576 CrossRefGoogle Scholar
Wang, Y., Yam, R.C.M., Zuoc, M.J., Tse, P., A comprehensive reliability method for CNC lathes, Reliab. Eng. Syst. Safe. 72 (2001) 247–252 CrossRefGoogle Scholar
Yalaoui, A., Chu, C., Châtelet, E., Reliability allocation problem in a series-parallel systems, Reliab. Eng. Syst. Safe. 90 (2005) 55–61 CrossRefGoogle Scholar
Yager, R.R., On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Transactions on Systems, Man and Cybernetics 18 (1988) 183–190 CrossRefGoogle Scholar
Yang, J.E., Hwang, M.J., Sung, T.Y., Jin, Y., Application of genetic algorithm for reliability allocation in nuclear power plants, Reliab. Eng. Syst. Safe. 65 (1999) 229–238 CrossRefGoogle Scholar
Yeh, W.C., Hsieh, T.J., Solving reliability redundancy allocation problems using an artificial bee colony algorithm, Comput. Oper. Res. 38 (2011) 1465–1473 CrossRefGoogle Scholar
Zhang, K.S., Li, W.J., Wei, H.Y.. A new method for Optimum Allocation of Design Requirements in Aircraft Conceptual Design, Chinese J. Aeronautics 19 (2006) 203–211 CrossRefGoogle Scholar