Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization
O Ogunfowora, H Najjaran - Journal of Manufacturing Systems, 2023 - Elsevier
Abstract Systems and machines undergo various failure modes that result in machine health
degradation, so maintenance actions are required to restore them back to a state where they …
degradation, so maintenance actions are required to restore them back to a state where they …
Applications of Reinforcement Learning for maintenance of engineering systems: A review
AP Marugán - Advances in Engineering Software, 2023 - Elsevier
Nowadays, modern engineering systems require sophisticated maintenance strategies to
ensure their correct performance. Maintenance has become one of the most important tasks …
ensure their correct performance. Maintenance has become one of the most important tasks …
Digital twin in aerospace industry: A gentle introduction
L Li, S Aslam, A Wileman, S Perinpanayagam - IEEE Access, 2021 - ieeexplore.ieee.org
Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly
transformative technology with profound implications. Whether it be product development …
transformative technology with profound implications. Whether it be product development …
A survey on reinforcement learning in aviation applications
Reinforcement learning (RL) has emerged as a powerful tool for addressing complex
decision making problems in various domains, including aviation. This paper provides a …
decision making problems in various domains, including aviation. This paper provides a …
Towards efficient airline disruption recovery with reinforcement learning
Disruptions to airline schedules precipitate flight delays/cancellations and significant losses
for airline operations. The goal of the integrated airline recovery problem is to develop an …
for airline operations. The goal of the integrated airline recovery problem is to develop an …
Configuration optimization of an off-grid multi-energy microgrid based on modified NSGA-II and order relation-TODIM considering uncertainties of renewable energy …
Z Lu, Y Gao, C Xu, Y Li - Journal of Cleaner Production, 2023 - Elsevier
This study develops a two-stage hybrid decision framework to configure an off-grid multi-
energy microgrid (MEMG) while considering uncertainties in renewable energy resources …
energy microgrid (MEMG) while considering uncertainties in renewable energy resources …
An end-to-end deep reinforcement learning method based on graph neural network for distributed job-shop scheduling problem
Abstract Distributed Job-shop Scheduling Problem (DJSP) is a hotspot in industrial and
academic fields due to its valuable application in the real-life productions. For DJSP, the …
academic fields due to its valuable application in the real-life productions. For DJSP, the …
Solve routing problems with a residual edge-graph attention neural network
For NP-hard combinatorial optimization problems, it is usually challenging to find high-
quality solutions in polynomial time. Designing either an exact algorithm or an approximate …
quality solutions in polynomial time. Designing either an exact algorithm or an approximate …
Condition-based maintenance with reinforcement learning for refrigeration systems with selected monitored features
Worldwide, buildings are responsible for almost 30% of energy consumption, and those
buildings that intensively use refrigeration systems, such as supermarkets and grocery …
buildings that intensively use refrigeration systems, such as supermarkets and grocery …
Multi-agent deep reinforcement learning-based maintenance optimization for multi-dependent component systems
Manufacturing systems consist of a set of interdependent components. However, addressing
the dependence between these components remains a challenge in both maintenance …
the dependence between these components remains a challenge in both maintenance …