A Generalized Age-Replacement Model

1992 ◽  
Vol 6 (4) ◽  
pp. 525-541 ◽  
Author(s):  
Stephan G. Vanneste
Keyword(s):  
Optimization Procedure ◽  
Control Limit ◽  
Maintenance Policy ◽  
Unimodal Function ◽  
Replacement Model ◽  
Markov Decision ◽  
Optimal Maintenance ◽  
Age Replacement ◽  
Replacement Problem ◽  
Optimal Maintenance Policy

Four practically important extensions of the classical age-replacement problem are analyzed using Markov decision theory: (1) opportunity maintenance, (2) imperfect repair, (3) non-zero repair times, and (4) Markov degradation of the working unit. For this general model, we show that the optimal maintenance policy is of the control limit type and that the average costs are a unimodal function of the control limit. An efficient optimization procedure is provided to find the optimal policy and its average costs. The analysis extends and unifies existing results.

Control Improvement of the Reactor Protection System

2011 ◽  
Vol 71-78 ◽  
pp. 4199-4202
Author(s):  
Bo Ya Zhao ◽  
Song Yang ◽  
Zhe Zhang ◽  
Ri Sheng Sun
Keyword(s):  
Preventive Maintenance ◽  
Protection System ◽  
Maintenance Policy ◽  
Block System ◽  
Replacement Model ◽  
Optimal Maintenance ◽  
Age Replacement ◽  
Reactor Protection System ◽  
Optimal Maintenance Policy ◽  
Random Failures

In this paper an optimal maintenance policy for a Reactor Protection System (RPS) for a nuclear plant was developed. RPS consists of continuously operating sub-systems that were subject to random failures. A block system diagram for RPS had been proposed that facilitates analyzing of individual sub-systems separately. The proposed maintenance policy is the Age Replacement model, which incorporated both corrective and preventive maintenances. A Markov model was used to optimize the preventive maintenance interval of those sub-systems whose failure and repair rates were exponentially distributed. Finally, a sensitivity analysis had been performed and recommendations for maintaining the required RPS availability have been suggested.

Maintenance Decision Model for a Two-Machine Production Line With Multistage Degradation Machines

2018 ◽  
Author(s):  
Yunyi Kang ◽  
Feng Ju
Keyword(s):  
Decision Model ◽  
Production Systems ◽  
Maintenance Policy ◽  
Multi Stage ◽  
Markov Decision ◽  
Optimal Maintenance ◽  
Markov Decision Model ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Optimal Maintenance Policy

In this work, we develop preventative maintenance policies on two-machine-and-one-buffer production systems with machines subject to multi-stage degradation. Condition-based maintenance policies are generated for both machines, with consideration on both the machine degradation stages and the buffer level. Moreover, the policies are flexible, allowing a machine to be recovered to any better operating state, while merely recovering to the best operating state is possible in many previous work. A Markov decision model is formulated to find the optimal maintenance policy and computational experiments show that the policies improve the performance of a system in finite production runs.

MAINTENANCE OPTIMIZATION OF EQUIPMENT BY LINEAR PROGRAMMING

2005 ◽  
Vol 20 (1) ◽  
pp. 183-193 ◽  
Author(s):  
Archana Jayakumar ◽  
Sohrab Asgarpoor
Keyword(s):  
Linear Programming ◽  
Preventive Maintenance ◽  
Mathematical Optimization ◽  
Optimal Solution ◽  
Cost Effective ◽  
Maintenance Policy ◽  
Markov Decision ◽  
Random Failure ◽  
Optimal Maintenance ◽  
Optimal Maintenance Policy

Optimal levels of preventive maintenance performed on any system ensures cost-effective and reliable operation of the system. In this paper a component with deterioration and random failure is modeled using Markov processes while incorporating the concept of minor and major preventive maintenance. The optimal mean times to preventive maintenance (both minor and major) of the component is determined by maximizing its availability with respect to mean time to preventive maintenance. Mathematical optimization programs Maple 7 and Lingo 7 are used to find the optimal solution, which is illustrated using a numerical example. Further, an optimal maintenance policy is obtained using Markov Decision Processes (MDPs). Linear Programming (LP) is utilized to implement the MDP problem.

An Optimal Maintenance Policy for a Queueing System Server Under Periodic Observation

1997 ◽  
Vol 04 (04) ◽  
pp. 357-367 ◽  
Author(s):  
Junji Koyanagi ◽  
Hajime Kawai
Keyword(s):  
Preventive Maintenance ◽  
Queueing System ◽  
Observation Time ◽  
Maintenance Policy ◽  
Corrective Maintenance ◽  
Markov Decision ◽  
Poisson Stream ◽  
Optimal Maintenance ◽  
Optimal Maintenance Policy ◽  
Time Of Failure

This paper describes an optimal maintenance policy for an M/M/1 queueing system server. Customers arrive at the system in a Poisson stream and are served by the exponential server. After a random time, the server is interrupted by a failure and this failure is detected through regularly timed observations. We begin corrective maintenance when we detect the failure. Through the failure of the server, we lose the customers in the system at the time of failure, as well as the customers that arrive between the failure and the completion of corrective maintenance. However, it is possible to avoid the failure and subsequent corrective maintenance by performing preventive maintenance at observation time. It is true that customers in the system at the start of preventive maintenance and those that arrive prior to its completion are lost. Since the queueing system should serve as many customers as possible, our objective is to minimize the number of lost customers. We then formulate this problem as a semi-Markov decision process and prove the switch curve structure of the optimal policy.

scholarly journals A Near-Optimal Maintenance Policy for Automated DR Devices

2016 ◽  
Vol 7 (3) ◽  
pp. 1411-1419 ◽  
Author(s):  
Carlos Abad ◽  
Garud Iyengar
Keyword(s):  
Maintenance Policy ◽  
Optimal Maintenance ◽  
Optimal Maintenance Policy
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