Cover Image

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Khadivi, Maziyar, Charter, Todd, Yaghoubi, Marjan, Jalayer, Masoud, Ahang, Maryam, Shojaeinasab, Ardeshir, Najjaran, Homayoun
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Neural and Evolutionary Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN
by: Ahang, Maryam, et al.
Published: (2022)

A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems
by: Ahang, Maryam, et al.
Published: (2026)

A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling
by: Khadivi, Maziyar, et al.
Published: (2024)

Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
by: Ahang, Maryam, et al.
Published: (2024)

Automated Optimal Layout Generator for Animal Shelters: A framework based on Genetic Algorithm, TOPSIS and Graph Theory
by: Jalayer, Arghavan, et al.
Published: (2024)

Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework
by: Ahang, Maryam, et al.
Published: (2024)

A computational approach to visual ecology with deep reinforcement learning
by: Sokoloski, Sacha, et al.
Published: (2024)

Sequential sampling without comparison to boundary through model-free reinforcement learning
by: Esmaily, Jamal, et al.
Published: (2024)

Use of a genetic algorithm in university scheduling for equitable and efficient determination of teaching assignments
by: Bensky, Tom, et al.
Published: (2025)

Automatic programming via large language models with population self-evolution for dynamic job shop scheduling problem
by: Huang, Jin, et al.
Published: (2024)

An exploration for higher efficiency in multi objective optimisation with reinforcement learning
by: Aydin, Mehmet Emin
Published: (2025)

Quantum memristors for neuromorphic quantum machine learning
by: Lamata, Lucas
Published: (2024)

An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring
by: Abbasi, Mostafa, et al.
Published: (2025)

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling
by: Mencaroni, Andrea, et al.
Published: (2026)

A Methodology for Effective Surrogate Learning in Complex Optimization
by: Harada, Tomohiro, et al.
Published: (2026)

Modelling of automotive steel fatigue lifetime by machine learning method
by: Yasniy, Oleh, et al.
Published: (2025)

Optimal feature rescaling in machine learning based on neural networks
by: Vitrò, Federico Maria, et al.
Published: (2024)

A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problem
by: Zou, Yuji, et al.
Published: (2024)

An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM
by: Abdalla, Hemn Barzan, et al.
Published: (2023)

Evolving choice hysteresis in reinforcement learning: comparing the adaptive value of positivity bias and gradual perseveration
by: Hoxha, Isabelle, et al.
Published: (2024)

Bellman Memory Units: A neuromorphic framework for synaptic reinforcement learning with an evolving network topology
by: Banerjee, Shreyan, et al.
Published: (2025)

A Multiplication-Free Spike-Time Learning Algorithm and its Efficient FPGA Implementation for On-Chip SNN Training
by: Mirsadeghi, Maryam, et al.
Published: (2026)

Chaos-based reinforcement learning with TD3
by: Matsuki, Toshitaka, et al.
Published: (2024)

Implementing engrams from a machine learning perspective: the relevance of a latent space
by: de Lucas, J Marco
Published: (2024)

Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction
by: Mencaroni, Andrea, et al.
Published: (2025)

Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs
by: Ruddick, Julian, et al.
Published: (2022)

Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
by: Nimbekar, Anagha, et al.
Published: (2024)

Adding numbers with spiking neural circuits on neuromorphic hardware: A building block for future hybrid systems
by: von Seeler, Oskar, et al.
Published: (2025)

A systematic review of metaheuristics-based and machine learning-driven intrusion detection systems in IoT
by: Ahsan, Mohammad Shamim, et al.
Published: (2025)

Benchmarking Hebbian learning rules for associative memory
by: Lansner, Anders, et al.
Published: (2023)

Neural auto-association with optimal Bayesian learning
by: Knoblauch, Andreas
Published: (2024)

Hardware-In-The-Loop Training of a 4f Optical Correlator with Logarithmic Complexity Reduction for CNNs
by: Pes, Lorenzo, et al.
Published: (2025)

Evolutionary Discovery of Sequence Acceleration Methods for Slab Geometry Neutron Transport
by: Patel, Japan K., et al.
Published: (2025)

Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence
by: He, Xiang, et al.
Published: (2025)

DelRec: learning delays in recurrent spiking neural networks
by: Queant, Alexandre, et al.
Published: (2025)

Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
by: Zhang, Huan, et al.
Published: (2023)

Composite FORCE learning of chaotic echo state networks for time-series prediction
by: Li, Yansong, et al.
Published: (2022)

A fuzzy adaptive evolutionary-based feature selection and machine learning framework for single and multi-objective body fat prediction
by: Keivanian, Farshid, et al.
Published: (2023)

Accelerating evolutionary exploration through language model-based transfer learning
by: Reissmann, Maximilian, et al.
Published: (2024)

Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization
by: Dong, Guodan, et al.
Published: (2024)