TY - JOUR
T1 - Joint optimization of X ̄ control chart and preventive maintenance policies
T2 - A discrete-time Markov chain approach
AU - Xiang, Yisha
N1 - Funding Information:
The author would like to thank the editor and referees for their insightful comments and suggestions. The author would also like to thank Professor David Coit from Rutgers University for his review of the revised manuscript and his helpful comments. The work of the author was supported by Chinese Ministry of Education under Grant 11YJC630228 , and by the Fundamental Research Funds for the Central Universities under Grant 13wkpy15 .
PY - 2013/9/1
Y1 - 2013/9/1
N2 - Statistical process control and maintenance planning have long been treated as two separate problems. The interdependence between these two activities has not been adequately addressed in the literature, despite their apparent connections. Information obtained in the course of statistical process control signals the need for possible maintenance actions, and thus, affects the preventive maintenance schedules. Preventive maintenance actions can prevent a production process from further deterioration and improve product quality in conjunction with statistical process control. This paper presents an integrated model for the joint optimization of statistical process control and preventive maintenance. The proposed model is developed for a production process that deteriorates according to a discrete-time Markov chain. It is assumed that preventive maintenance is imperfect, and both preventive and corrective maintenance are instantaneous. The formulation of the deterioration process with maintenance interventions, formulated as a Markov chain, provides a breakthrough in designing an efficient solution algorithm and obtaining analytical results. A numerical example is used to illustrate the proposed integrated statistical process control and preventive maintenance policies. Sensitivity analysis is conducted to analyze the impact of model parameters on optimal policies. Sensitivity analysis further indicates the interrelationship between statistical process control and maintenance actions. Numerical results indicate that potential cost savings can be achieved from the proposed integrated policies.
AB - Statistical process control and maintenance planning have long been treated as two separate problems. The interdependence between these two activities has not been adequately addressed in the literature, despite their apparent connections. Information obtained in the course of statistical process control signals the need for possible maintenance actions, and thus, affects the preventive maintenance schedules. Preventive maintenance actions can prevent a production process from further deterioration and improve product quality in conjunction with statistical process control. This paper presents an integrated model for the joint optimization of statistical process control and preventive maintenance. The proposed model is developed for a production process that deteriorates according to a discrete-time Markov chain. It is assumed that preventive maintenance is imperfect, and both preventive and corrective maintenance are instantaneous. The formulation of the deterioration process with maintenance interventions, formulated as a Markov chain, provides a breakthrough in designing an efficient solution algorithm and obtaining analytical results. A numerical example is used to illustrate the proposed integrated statistical process control and preventive maintenance policies. Sensitivity analysis is conducted to analyze the impact of model parameters on optimal policies. Sensitivity analysis further indicates the interrelationship between statistical process control and maintenance actions. Numerical results indicate that potential cost savings can be achieved from the proposed integrated policies.
KW - Maintenance Statistical process control Markov processes
UR - http://www.scopus.com/inward/record.url?scp=84876962728&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2013.02.041
DO - 10.1016/j.ejor.2013.02.041
M3 - Article
AN - SCOPUS:84876962728
SN - 0377-2217
VL - 229
SP - 382
EP - 390
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 2
ER -