A discrete event simulation design for block-based maintenance planning under random machine usage

Existing research on block-based preventive maintenance planning generally assumes that machines are either used continuously, or that times until failure do not depend on the actual usage of machines. In practice, however, it is often more realistic to assume that machines are not used continuously and that they only deteriorate when they are used. In this paper, we present a discrete event simulation design for optimizing block-based preventive maintenance under random machine usage. Furthermore, we will deliver some preliminary results that indicate that the optimal preventive maintenance interval not only depends on the utilization rate of the machine, but also on the usage pattern.

[1]  Peter Knights,et al.  A decision-making framework to integrate maintenance contract conditions with critical spares management , 2014, Reliab. Eng. Syst. Saf..

[2]  Athena Zitrou,et al.  Robustness of maintenance decisions: Uncertainty modelling and value of information , 2013, Reliab. Eng. Syst. Saf..

[3]  Anahita Khojandi,et al.  Optimal planning of life-depleting maintenance activities , 2014 .

[4]  Frank P. A. Coolen,et al.  Predictive inference for system reliability after common-cause component failures , 2015, Reliab. Eng. Syst. Saf..

[5]  Andrew K. S. Jardine,et al.  An optimal burn‐in preventive‐replacement model associated with a mixture distribution , 2007, Qual. Reliab. Eng. Int..

[6]  Le Cao,et al.  Optimal tool replacement with product quality deterioration and random tool failure , 2015 .

[7]  Tongdan Jin,et al.  Degradation Modeling and Maintenance Decisions Based on Bayesian Belief Networks , 2014, IEEE Transactions on Reliability.

[8]  J. L. Guardado,et al.  Analytical method for optimization of maintenance policy based on available system failure data , 2015, Reliab. Eng. Syst. Saf..

[9]  Shahrul Kamaruddin,et al.  An overview of time-based and condition-based maintenance in industrial application , 2012, Comput. Ind. Eng..

[10]  Arjan S. Dijkstra,et al.  Cost benefits of postponing time-based maintenance under lifetime distribution uncertainty , 2015, Reliab. Eng. Syst. Saf..

[11]  Haibo Jin,et al.  A novel optimal preventive maintenance policy for a cold standby system based on semi-Markov theory , 2014, Eur. J. Oper. Res..

[12]  Tangbin Xia,et al.  Production-driven opportunistic maintenance for batch production based on MAM-APB scheduling , 2015, Eur. J. Oper. Res..

[13]  Toshio Nakagawa,et al.  Which is better for replacement policies with continuous or discrete scheduled times? , 2015, Eur. J. Oper. Res..

[14]  R. Barlow,et al.  Optimum Preventive Maintenance Policies , 1960 .

[15]  Raha Akhavan-Tabatabaei,et al.  Time and inventory dependent optimal maintenance policies for single machine workstations: An MDP approach , 2013, Eur. J. Oper. Res..

[16]  Hoang Pham,et al.  A Generalized Block Replacement Policy for a $k$ -Out-of-$n$ System With Respect to Threshold Number of Failed Components and Risk Costs , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[17]  Shey-Huei Sheu,et al.  An Optimal Age Replacement Policy for Multi-State Systems , 2013, IEEE Transactions on Reliability.

[18]  Haiping Zhu,et al.  An integrated model of statistical process control and maintenance based on the delayed monitoring , 2015, Reliab. Eng. Syst. Saf..