Cost Based Warranty Optimisation Using Genetic Algorithm
Warranty is a powerful marketing tool for the
manufacturer and a good protection for both the manufacturer and the
customer. However, warranty always involves additional costs to the
manufacturer, which depend on product reliability characteristics and
warranty parameters. This paper presents an approach to optimisation
of warranty parameters for known product failure distribution to
reduce the warranty costs to the manufacturer while retaining the
promotional function of the warranty. Combination free replacement
and pro-rata warranty policy is chosen as a model and the length of
free replacement period and pro-rata policy period are varied, as well
as the coefficients that define the pro-rata cost function. Multiparametric
warranty optimisation is done by using genetic algorithm.
Obtained results are guideline for the manufacturer to choose the
warranty policy that minimises the costs and maximises the profit.
[1] D. Stamenkovic, V. Popovic, V. Spasojevic Brkic, and J. Radivojevic,
"An approach to optimization of warranty policy - a case study," in
Proc. 21st European Congress on Maintenance and Asset Management
- Euromaintenance 2012, Belgrade, 2012, pp. 24-32.
[2] D. Stamenkovic, V. Popovic, and D. Aleksendric, "Fully renewing
combination free replacement and pro-rata warranty cost assessment
using Monte Carlo simulation," in Proc. 18th ISSAT International
Conference on Reliability and Quality in Design, Boston, 2012, pp. 315-
319.
[3] D. Stamenkovic, and V. Popovic, "Warranty optimisation based on the
prediction of costs to the manufacturer using neural network model and
Monte Carlo simulation," Int. J. Syst. Sci., to be published.
[4] D. Stamenkovic, V. Popovic, V. Spasojevic Brkic, and J. Radivojevic,
"Combination free replacement and pro-rata warranty policy
optimization model," Journal of Applied Engineering Science, vol. 9,
pp. 457-464, 2011.
[5] W.R. Blischke, and D.N.P. Murthy, Product Warranty Handbook. New
York, NY: Marcel Dekker, 1996.
[6] A. Mitra, and J.G. Patankar, "Market share and warranty costs for
renewable warranty programs," Int. J. Prod. Econ., vol. 50, pp. 155-168,
1997.
[7] W.R. Blischke, M.R. Karim, and D.N.P. Murthy, Warranty Data
Collection and Analysis. London, UK: Springer Verlag, 2011.
[8] G. Yang, Life Cycle Reliability Engineering. Hoboken, NJ: John Wiley
& Sons, 2007.
[9] Global Optimization Toolbox User-s Guide. Natick, MA: The
MathWorks, Inc., 2012.
[10] R.L. Haupt, and S.E. Haupt, Practical Genetic Algorithms (2nd ed.).
Hoboken, NJ: John Wiley & Sons, 2004.
[11] D.A. Ratkowsky, Handbook of Nonlinear Regression Models. New
York, NY: Marcel Dekker, 1990.
[12] V. Popovic, D. Stamenkovic, and B. Rakicevic, "Choosing the right
warranty policy - from the customer-s to the manufacturer-s point of
view," in Proc. 2012 International Conference on Pure and Applied
Mathematics, Paris, 2012, paper no. P00024.
[1] D. Stamenkovic, V. Popovic, V. Spasojevic Brkic, and J. Radivojevic,
"An approach to optimization of warranty policy - a case study," in
Proc. 21st European Congress on Maintenance and Asset Management
- Euromaintenance 2012, Belgrade, 2012, pp. 24-32.
[2] D. Stamenkovic, V. Popovic, and D. Aleksendric, "Fully renewing
combination free replacement and pro-rata warranty cost assessment
using Monte Carlo simulation," in Proc. 18th ISSAT International
Conference on Reliability and Quality in Design, Boston, 2012, pp. 315-
319.
[3] D. Stamenkovic, and V. Popovic, "Warranty optimisation based on the
prediction of costs to the manufacturer using neural network model and
Monte Carlo simulation," Int. J. Syst. Sci., to be published.
[4] D. Stamenkovic, V. Popovic, V. Spasojevic Brkic, and J. Radivojevic,
"Combination free replacement and pro-rata warranty policy
optimization model," Journal of Applied Engineering Science, vol. 9,
pp. 457-464, 2011.
[5] W.R. Blischke, and D.N.P. Murthy, Product Warranty Handbook. New
York, NY: Marcel Dekker, 1996.
[6] A. Mitra, and J.G. Patankar, "Market share and warranty costs for
renewable warranty programs," Int. J. Prod. Econ., vol. 50, pp. 155-168,
1997.
[7] W.R. Blischke, M.R. Karim, and D.N.P. Murthy, Warranty Data
Collection and Analysis. London, UK: Springer Verlag, 2011.
[8] G. Yang, Life Cycle Reliability Engineering. Hoboken, NJ: John Wiley
& Sons, 2007.
[9] Global Optimization Toolbox User-s Guide. Natick, MA: The
MathWorks, Inc., 2012.
[10] R.L. Haupt, and S.E. Haupt, Practical Genetic Algorithms (2nd ed.).
Hoboken, NJ: John Wiley & Sons, 2004.
[11] D.A. Ratkowsky, Handbook of Nonlinear Regression Models. New
York, NY: Marcel Dekker, 1990.
[12] V. Popovic, D. Stamenkovic, and B. Rakicevic, "Choosing the right
warranty policy - from the customer-s to the manufacturer-s point of
view," in Proc. 2012 International Conference on Pure and Applied
Mathematics, Paris, 2012, paper no. P00024.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52260", author = "Dragan D. Stamenkovic and Vladimir M. Popovic", title = "Cost Based Warranty Optimisation Using Genetic Algorithm", abstract = "Warranty is a powerful marketing tool for the
manufacturer and a good protection for both the manufacturer and the
customer. However, warranty always involves additional costs to the
manufacturer, which depend on product reliability characteristics and
warranty parameters. This paper presents an approach to optimisation
of warranty parameters for known product failure distribution to
reduce the warranty costs to the manufacturer while retaining the
promotional function of the warranty. Combination free replacement
and pro-rata warranty policy is chosen as a model and the length of
free replacement period and pro-rata policy period are varied, as well
as the coefficients that define the pro-rata cost function. Multiparametric
warranty optimisation is done by using genetic algorithm.
Obtained results are guideline for the manufacturer to choose the
warranty policy that minimises the costs and maximises the profit.", keywords = "costs, genetic algorithm, optimisation, warranty.", volume = "7", number = "5", pages = "811-4", }