Maintenance Policy Optimization through Data Analysis

Call for Papers

Increasingly intense competitive pressure has driven many firms to seek every possible source of competitive advantage. Competitive advantages can be achieved through optimising existing processes such as plant operation and/or maintenance. Preventive maintenance (PM) and condition-based maintenance (CBM) are interesting vehicles for these optimizations. Essentially, maintenance responds to cutting demands of operational expenses by optimising PM and/or CBM policies, which can also, in the long run, prolong the lifetime of the equipment maintained. Optimally, scheduling maintenance policies can ensure the equipment in a good condition and keep a certain level of availability.

In the literature, there are hundreds of maintenance models published. Most of them are developed by mathematicians, who might usually have limited knowledge of practical maintenance. Consequently, these models are seldom used in practical maintenance management. This mismatch can be avoided if maintenance models can be developed based on data collected from the field.

The advance in data collection techniques makes it increasingly easier to collect data of system performance. Such techniques can be, for example, the increasing miniaturization of radio frequency (RF) devices and microelectromechanical systems (MEMs), as well as the advances in wireless technologies. Researchers will need to process more data, most of which are streamline data. Such data can be used in maintenance policy scheduling. Potential topics include, but are not limited to:

• Condition monitoring, prognostics, and health management
• Expert elicitation in maintenance scheduling
• Human factors in maintenance
• Information, communication, and AI in maintenance
• Integrated maintenance and supply chain management
• Modeling of inspection, overhaul, and replacement
• Warranty and maintenance contract analysis

We are especially interested in research developed on the basis of field data.

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/ijqsr/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:

Lead Guest Editor

• Shaomin Wu, School of Applied Sciences, Cranfield University, Bedfordshire, UK

Guest Editors

• Wenbin Wang, Dong Ling School of Economics and Management, University of Science and Technology Beijing, Beijing, China
• Shey-Huei Sheu, Department of Statistics and Informatics Science, Providence University Taiwan, Taichung 43301, Taiwan
• Ralf Gitzel, ABB Corporate Research, Heidelberg, Germany