Condition-based joint maintenance optimization for a large-scale system with homogeneous units

ABSTRACT

A joint maintenance policy that simultaneously repairs multiple units is useful for large-scale systems where the setup cost to initiate the maintenance is generally higher than the repair costs. This study proposes a new method for scheduling maintenance activities in a large-scale system with homogeneous units that degrade over time. Specifically, we consider the maintenance type that renews all units at each maintenance activity, which is practically applicable for systems where the units need to be regularly maintained. To make the analysis computationally tractable, we discretize the health condition of each unit into a finite number of states. The proposed optimization formulation triggers the maintenance activity based on the fraction of units at each degradation state. Based on relevant asymptotic theories, we analytically obtain the optimal threshold in the fraction of units at each state that minimizes the long-run average maintenance cost. Our implementation results with a wide range of parameter settings show that the proposed maintenance strategy is more cost-effective than alternative strategies.

KEYWORDS:

• Asymptotic theories
• multi-unit systems
• reliability
• renewal process

Funding

This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2014R1A1A1005019), and in part by the U.S. National Science Foundation grants CMMI-1362513 and CMMI-1536924.

Acknowledgments

We would like to thank the Editor, Department Editor, and anonymous reviewers for their constructive comments on various aspects of this work.

Additional information

Notes on contributors

Young Myoung Ko

Young Myoung Ko is an assistant professor of Industrial and Management Engineering at Pohang University of Science and Technology (POSTECH), Korea. He received his B.S. and M.S. degrees in Industrial Engineering from Seoul National University, and his Ph.D. degree in Industrial Engineering from Texas A&M University in 1998, 2000, and 2011, respectively. His research focuses on the analysis and optimization of large-scale stochastic systems and covers the domains of service systems, telecommunication networks, energy-efficient infrastructure, and renewable energy systems.

Eunshin Byon

Eunshin Byon is an assistant professor with the Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor. She received her Ph.D. degree in Industrial and Systems Engineering from Texas A&M University, College Station, in 2010. Her research interests include data analytics, quality and reliability engineering, system infomatics and uncertainty quantification. She has received several Best Paper Awards including the 2015 Best Applications Paper Award from IIE Transactions on Quality & Reliability Engineering. Prof. Byon is a member of IIE, INFORMS, IEEE, and ASQ.