Journal
APPLIED MATHEMATICAL MODELLING
Volume 103, Issue -, Pages 221-237Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2021.10.028
Keywords
Dynamic replacement policy; Production process; Proportional hazards model; Component dependence; Dynamic programming
Funding
- National Natural Science Foundation of China [71671041, 72071044]
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This paper presents a dynamic inspection and replacement policy for a production system with two nonidentical units and an inventory system. Decision making is based on condition monitoring of the deterioration state of unit 1 and the failures of unit 2, aiming to minimize the long-run average cost rate through optimizing the inspection interval and replacement policy.
This paper proposes a dynamic inspection and replacement policy for a production system composed of two nonidentical units and an inventory system. Unit 1 deteriorates gradually and it is subject to soft failure. Unit 2 experiences hard failures that occur following a general lifetime distribution. The degradation processes of the two units are stochastically interdependent. Condition monitoring is performed to reveal the deterioration state of unit 1 when a certain number of production runs complete or when unit 2 fails, whichever comes first. At each inspection epoch, a decision is made on whether to replace unit 1 and/or unit 2. The objective is to jointly optimize the inspection interval and the replacement policy by minimizing the long-run average cost rate. The optimization problem is formulated in the semi-Markov decision process (SMDP) framework. Economic dependence and structural dependence are also integrated into the maintenance model. A matrix-based approximation method is employed to derive the SMDP quantities. The application of the proposed approach to a fuel oil supply system demonstrates the superiority of the proposed approach in saving operation and maintenance costs. The obtained graphical representation of the obtained policy is supportive during maintenance implementation. (c) 2021 Elsevier Inc. All rights reserved.
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