Condition-Based Maintenance for Queues With Degrading Servers

• Published in: IEEE transactions on automation science and engineering Vol. 16; no. 4; pp. 1750 - 1762
• Main Authors: Ejaz, Iqra, Alvarado, Michelle, Gautam, Natarajan, Gebraeel, Nagi, Lawley, Mark
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Catastrophic failure analysis; Computer simulation; Condition monitoring; Condition-based maintenance; Cost function; cost optimization; Costs; Decision making; Degradation; Discrete event systems; Industrial plants; Maintenance; Markov analysis; Markovian degradation; Queueing analysis; Queues; Queuing theory; Repair & maintenance; Repair time; Sensitivity analysis; Servers; single-server queues; Stability analysis; Steady state
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2019.2893870

The integration of condition monitoring with queueing systems to support decision making is not well explored. This paper addresses the impact of condition monitoring of the server on the system-level performance experienced by entities in a queueing system. The system consists of a queue with a single-server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady-state analysis to obtain performance measures (average queue length, average degradation, and so on). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality, and holding. The queue performance measures derived from steady-state analysis are benchmarked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Results indicate that the total cost function is convex and, thus, subject to an optimal repair policy. The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early stage policy repair decisions.