Solving flexible job shop scheduling problems via deep reinforcement learning
Flexible job shop scheduling problem (FJSSP), as a variant of the job shop scheduling
problem, has a larger solution space. Researchers are always looking for good methods to …
problem, has a larger solution space. Researchers are always looking for good methods to …
Deep reinforcement learning for solving resource constrained project scheduling problems with resource disruptions
H Cai, Y Bian, L Liu - Robotics and Computer-Integrated Manufacturing, 2024 - Elsevier
The resource-constrained project scheduling problem (RCPSP) is encountered in many
fields, including manufacturing, supply chain, and construction. Nowadays, with the rapidly …
fields, including manufacturing, supply chain, and construction. Nowadays, with the rapidly …
Hybrid quantum particle swarm optimization and variable neighborhood search for flexible job-shop scheduling problem
Y Xu, M Zhang, M Yang, D Wang - Journal of Manufacturing Systems, 2024 - Elsevier
The rise and integration of Industry 4.0 has led to a growing focus on the flexible job-shop
scheduling problem (FJSP). As an extension of the classic job-shop scheduling problem …
scheduling problem (FJSP). As an extension of the classic job-shop scheduling problem …
Design patterns of deep reinforcement learning models for job shop scheduling problems
S Wang, J Li, Q Jiao, F Ma - Journal of Intelligent Manufacturing, 2024 - Springer
Production scheduling has a significant role when optimizing production objectives such as
production efficiency, resource utilization, cost control, energy-saving, and emission …
production efficiency, resource utilization, cost control, energy-saving, and emission …
System-of-systems approach to spatio-temporal crowdsourcing design using improved PPO algorithm based on an invalid action masking
Spatio-temporal crowdsourcing (STC) is a typical case of complex system-of-systems (SoSs)
design, wherein the primary objective is to allocate real-time tasks to suitable groups of …
design, wherein the primary objective is to allocate real-time tasks to suitable groups of …
Mixed-batch scheduling to minimize total tardiness using deep reinforcement learning
JD Huang - Applied Soft Computing, 2024 - Elsevier
This study addresses the issue of scheduling batch machine to minimize total tardiness.
Vacuum heat treatment allows multiple jobs to be processed as a batch, as long as they do …
Vacuum heat treatment allows multiple jobs to be processed as a batch, as long as they do …
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem
J Lee, S Kee, M Janakiram, G Runger - arXiv preprint arXiv:2401.16580, 2024 - arxiv.org
Job shop scheduling problems are one of the most important and challenging combinatorial
optimization problems that have been tackled mainly by exact or approximate solution …
optimization problems that have been tackled mainly by exact or approximate solution …
Flow-Shop Scheduling Problem With Batch Processing Machines via Deep Reinforcement Learning for Industrial Internet of Things
The rapidly evolving Industrial Internet of Things (IIoT) is driving the transition from
conventional manufacturing to intelligent manufacturing. Intelligent shop scheduling, as one …
conventional manufacturing to intelligent manufacturing. Intelligent shop scheduling, as one …
An online hyper‐volume action bounding approach for accelerating the process of deep reinforcement learning from multiple controllers
A Aflakian, A Rastegarpanah… - Journal of Field …, 2024 - Wiley Online Library
This paper fuses ideas from reinforcement learning (RL), Learning from Demonstration
(LfD), and Ensemble Learning into a single paradigm. Knowledge from a mixture of control …
(LfD), and Ensemble Learning into a single paradigm. Knowledge from a mixture of control …
[PDF][PDF] An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems
C Zhao, N Deng - Mathematical Biosciences and Engineering, 2024 - aimspress.com
With the rise of Industry 4.0, manufacturing is shifting towards customization and flexibility,
presenting new challenges to meet rapidly evolving market and customer needs. To address …
presenting new challenges to meet rapidly evolving market and customer needs. To address …