Recently, many researchers focused on modeling non-monotonic hazard functions such as
bath-tube and hump shapes. However, most of their estimation methods are focused on …

Within this work, the challenge of developing maintenance planning solutions for networked
assets is considered. This is challenging due to the very nature of these systems which are …

Preventive Maintenance (PM) activities in semiconductor manufacturing are important for
equipment availability and reliability. Since PMs are down events that remove a tool from …

This thesis investigates the application of Deep Multi-Agent Reinforcement Learning
(DMARL) to problems within telecommunications and logistics. These sectors are exemplary …

Emergency/rescue helicopters are an essential part of the healthcare systems. Every
helicopter should be kept in its best possible operational mode to save the life of the people …

Phase-type distribution allows approximation of non-Markovian models, which permits to
analyze complex systems under Markovian deterioration. In addition, reliability data is often …

This paper compares the traditional approach against reinforcement learning algorithms to
find the optimal preventive maintenance policy for equipment composed of multi-non …

Taking optimal maintenance decisions is a challenging process as different maintenance
actions have different effects on the system. Maintenance is defined as a set of associated …

Oil palm industry is one of the key contributors to the Gross Domestic Product (GDP) and
Comprehensive National Strength (CNS) of Malaysia. According to the Department of …

Degradation is a natural phenomenon that occurs in different systems as a result of usage
and environmental exposure. Accordingly, there is a need for maintenance and inspection to …