Condition-based maintenance for multi-component systems: Modeling, structural properties, and algorithms
Zhicheng Zhu and
Yisha Xiang
IISE Transactions, 2021, vol. 53, issue 1, 88-100
Abstract:
Condition-Based Maintenance (CBM) is an effective maintenance strategy to improve system performance while lowering operating and maintenance costs. Real-world systems typically consist of a large number of components with various interactions among components. However, existing studies on CBM mainly focus on single-component systems. Multi-component CBM, which joins the components’ stochastic degradation processes and the combinatorial maintenance grouping problem, remains an open issue in the literature. In this article, we study the CBM optimization problem for multi-component systems. We first develop a multi-stage stochastic integer model with the objective of minimizing the total maintenance cost over a finite planning horizon. We then investigate the structural properties of a two-stage model. Based on the structural properties, two efficient algorithms are designed to solve the two-stage model. Algorithm 1 solves the problem to its optimality and Algorithm 2 heuristically searches for high-quality solutions based on Algorithm 1. Our computational studies show that Algorithm 1 obtains optimal solutions in a reasonable amount of time and Algorithm 2 can find high-quality solutions quickly. The multi-stage problem is solved using a rolling horizon approach based on the algorithms for the two-stage problem. Supplementary materials are available for this article. Go to the publisher’s online edition of IISE Transaction, datasets, additional tables, detailed proofs, etc.
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2020.1741740 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:53:y:2021:i:1:p:88-100
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20
DOI: 10.1080/24725854.2020.1741740
Access Statistics for this article
IISE Transactions is currently edited by Jianjun Shi
More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().