Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-26T22:11:44.916Z Has data issue: false hasContentIssue false

On bounds for some optimal policies in reliability

Published online by Cambridge University Press:  14 July 2016

Jie Mi*
Affiliation:
Florida International University and Anhui University
*
Postal address: Department of Statistics, Florida International University, University Park, Miami, FL 33199, USA. Email address: [email protected]

Abstract

Often in the study of reliability and its applications, the goal is to maximize or minimize certain reliability characteristics or some cost functions. For example, burn-in is a procedure used to improve the quality of products before they are used in the field. A natural question which arises is how long the burn-in procedure should last in order to maximize the mean residual life or the conditional survival probability. In the literature, an upper bound for the optimal burn-in time is obtained by assuming that the underlying distribution of the products has a bathtub-shaped failure rate function; however, no lower bound is available. A similar question arises in studying replacement policy, warranty policy, and inspection models. This article gives a lower bound for the optimal burn-in time, and lower and upper bounds for the optimal replacement and warranty policies, under the same bathtub-shape assumption.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2002 

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

Barlow, R. E., and Proschan, F. (1965). Mathematical Theory of Reliability. John Wiley, New York.Google Scholar
Block, H. W., and Savits, T. H. (1997). Burn-in. Statist. Sci. 12, 113.Google Scholar
Block, H. W., Borges, W. S., and Savits, T. H. (1985). Age-dependent minimal repair. J. Appl. Prob. 22, 370385.Google Scholar
Block, H. W., Jong, Y. K., and Savits, T. H. (1999). Bathtub function and burn-in. Prob. Eng. Inf. Sci. 13, 497507.CrossRefGoogle Scholar
Haines, A. L., and Singpurwalla, N. D. (1974). Some contribution to the stochastic characterization of wear. In Reliability and Biometry, eds Proschan, F. and Serfling, J., SIAM, Philadelphia, pp. 4780.Google Scholar
Joe, H., and Proschan, F. (1984). Percentile residual life functions. Operat. Res. 32, 668678.Google Scholar
Leemis, L. M., and Beneke, M. (1990). Burn-in models and methods: a review. IIE Trans. 22, 172180.CrossRefGoogle Scholar
Mi, J. (1993). Discrete bathtub failure rate and upside-down bathtub mean residual life. Naval Res. Logistics 40, 361371.Google Scholar
Mi, J. (1994). Burn-in and maintenance policies. Adv. Appl. Prob. 26, 207221.Google Scholar
Mi, J. (1995). Bathtub failure rate and upside-down bathtub mean residual life. IEEE Trans. Reliab. 44, 388391.Google Scholar
Mi, J. (1996). Minimizing some cost functions related to both burn-in and field use. Operat. Res. 44, 497500.Google Scholar
Mi, J. (1997). Warranty policies and burn-in. Naval Res. Logistics 44, 199209.Google Scholar
Mi, J. (1998). Some comparison results of system availability. Naval Res. Logistics 45, 205218.Google Scholar
Mi, J. (2002). Age-replacement policy and optimal work size. J. Appl. Prob. 39, 296311.Google Scholar
Rajarshi, S., and Rajarshi, M. B. (1988). Bathtub distributions: a review. Commun. Statist. Theory Meth. 17, 25972621.Google Scholar
Ross, S. (1996). Stochastic Processes. John Wiley, New York.Google Scholar
Sarkar, J., and Sarkar, S. (2000). Availability of a periodically inspected system under perfect repair. J. Statist. Planning Infer. 91, 7790.CrossRefGoogle Scholar
Yang, Y., and Klutke, G.-A. (2000). Improved inspection schemes for deteriorating equipment. Prob. Eng. Inf. Sci. 14, 445460.Google Scholar