Intelligent Scheduling Based on Reinforcement Learning Approaches: Applying Advanced Q-Learning and State–Action–Reward–State–Action Reinforcement Learning Models for the Optimisation of Job Shop Scheduling Problems

Intelligent Scheduling Based on Reinforcement Learning Approaches: Applying Advanced Q-Learning and State–Action–Reward–State–Action Reinforcement Learning Models for the Optimisation of Job Shop Scheduling Problems

Flexible job shop scheduling problems (FJSPs) have attracted significant research interest because they can considerably increase production efficiency in terms of energy, cost and time; they are considered the main part of the manufacturing systems which frequently need to be resolved to manage the...

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Bibliographic Details
Main Authors: Atefeh Momenikorbekandi, Maysam Abbod
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Electronics
Subjects:
flexible job shop scheduling problems
reinforcement learning (RL)
QRL
SARSA RL
single-job shops and multi-machine job shops
Online Access:https://www.mdpi.com/2079-9292/12/23/4752

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https://www.mdpi.com/2079-9292/12/23/4752

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