Hostname: page-component-89b8bd64d-ktprf Total loading time: 0 Render date: 2026-05-08T06:45:34.799Z Has data issue: false hasContentIssue false
  • English
  • Français

SINGLE-MACHINE MULTIPLE-RECIPE PREDICTIVE MAINTENANCE

Published online by Cambridge University Press:  28 March 2013

Yiwei Cai ,
John J. Hasenbein ,
Erhan Kutanoglu  and
Melody Liao
Yiwei Cai
Affiliation:
PayPal, Inc., 7700 W. Parmer Lane, Austin, TX 78729 E-mail: ewaycai@gmail.com
John J. Hasenbein
Affiliation:
Graduate Program in Operations Research and Industrial Engineering, Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712 E-mails: jhas@mail.utexas.edu; erhank@mail.utexas.edu; m.liao@mail.utexas.edu
Erhan Kutanoglu
Affiliation:
Graduate Program in Operations Research and Industrial Engineering, Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712 E-mails: jhas@mail.utexas.edu; erhank@mail.utexas.edu; m.liao@mail.utexas.edu
Melody Liao
Affiliation:
Graduate Program in Operations Research and Industrial Engineering, Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712 E-mails: jhas@mail.utexas.edu; erhank@mail.utexas.edu; m.liao@mail.utexas.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper studies a multiple-recipe predictive maintenance problem with M/G/1 queueing effects. The server degrades according to a discrete-time Markov chain and we assume that the controller knows both the machine status and the current number of jobs in the system. The controller's objective is to minimize total discounted costs or long-run average costs which include preventative and corrective maintenance costs, holdings costs, and possibly production costs. An optimal policy determines both when to perform maintenance and which type of job to process. Since the policy takes into account the machine's degradation status, such control decisions are known as predictive maintenance policies. In the single-recipe case, we prove that the optimal policy is monotone in the machine status, but not in the number of jobs in the system. A similar monotonicity result holds in the two-recipe case. Finally, we provide computational results indicating that significant savings can be realized when implementing a predictive maintenance policies instead of a traditional job-based threshold policy for preventive maintenances.

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 27 , Issue 2 , April 2013 , pp. 209 - 235
Copyright
Copyright © Cambridge University Press 2013

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.)

Article purchase

Temporarily unavailable

References

1.Xpress-mosel user guide (2004). Englewood Cliffs, NY: Dash Optimization Ltd.Google Scholar
2.Baek, K.H., Jung, Y., Min, G.J., Kang, C., Cho, H. K., & Moon, J.T. (2005). Chamber maintenance and fault detection technique for a gate etch process via self-excited electron resonance spectroscopy. Journal of Vacuum Science and Technology B 23(1): 125–129.CrossRefGoogle Scholar
3.Bertsekas, D.P. (2007). Dynamic programming and optimal control. Vol. I & II. Belmont, MA: Athena Scientific.Google Scholar
4.Cassady, C.R. & Kutanoglu, E. (2003). Minimizing job tardiness using integrated preventive maintenance planning and production scheduling. IIE Transactions 35: 503–513.CrossRefGoogle Scholar
5.Dekker, R. (1996). Applications of maintenance optimization models: a review and analysis. Reliability Engineering and System Safety 51: 229–240.CrossRefGoogle Scholar
6.Hsu, L.F. (1992). Optimal preventive maintenance policies in an {M/G}/1 queue-like production system. European Journal of Operational Research 58(1): 112–122.CrossRefGoogle Scholar
7.Iravani, S.M.R. & Duenyas, I. (2002). Integrated maintenance and production control of a deteriorating production system. IIE Transactions 34: 423–435.CrossRefGoogle Scholar
8.Kaufman, D. & Lewis, M. (2007). Machine maintenance with workload considerations. Naval Research Logistics 54: 750–766.CrossRefGoogle Scholar
9.Lee, J. (1998). Teleservice engineering in manufacturing: challenges and opportunities. International Journal of Machine Tools and Manufacture 38: 901–910.CrossRefGoogle Scholar
10.McCall, J.J. (1965). Maintenance policies for stochastically failing equipment: a survey. Management Science 11(5): 493–524.CrossRefGoogle Scholar
11.Mobley, R.K. (2002). An introduction to predictive maintenance. Oxford: Butterworth-Heinemann.Google Scholar
12.Ruiz, R., García-Díaz, J.C., & Maroto, C. (2007). Considering scheduling and preventive maintenance in the flowshop sequencing problem. Computers and Operations Research 34(11): 3314–3330.CrossRefGoogle Scholar
13.Sennott, L. (1989). Average cost semi-{M}arkov decision processes and the control of queueing systems. Probability in the Engineering and Informational Sciences 3: 247–272.CrossRefGoogle Scholar
14.Sloan, T.W. & Shanthikumar, J.G. (2000). Combined production and maintenance scheduling for a multiple-product, single-machine production system. Production and Operations Management 9(4): 379–399.CrossRefGoogle Scholar
15.Van der Duyn Schouten, F.A. & Vanneste, S.G. (1995). Maintenance optimization of a production system with buffer capacity. European Journal of Operational Research 82(2): 323–338.CrossRefGoogle Scholar
16.Yang, Z., Djurdjanovic, D., & Ni, J. (2007). Maintenance scheduling for a manufacturing system of machines with adjustable throughput. IIE Transactions 39: 1111–1125.CrossRefGoogle Scholar
17.Yao, X. (2003) Optimal preventive maintenance policies for unreliable queueing and production systems. PhD thesis, University of Maryland.Google Scholar
18.Zhou, J., Djurdjanovic, D., Ivy, J., & Ni, J. (2007). Integrated reconfiguration and age-based preventive maintenance decision making. IIE Transactions, 39: 1085–1102.CrossRefGoogle Scholar