A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints
Abstract Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize...
Saved in:
| Main Authors: | Xiaoting Dong, Guangxi Wan, Peng Zeng |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01828-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
by: Oludolapo Akanni Olanrewaju, et al.
Published: (2024-11-01) -
A Robust Heuristics for the Online Job Shop Scheduling Problem
by: Hugo Zupan, et al.
Published: (2024-12-01) -
A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling
by: Jian Li, et al.
Published: (2025-07-01) -
A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration
by: Dekun Wang, et al.
Published: (2025-03-01) -
Multi-Agent Reinforcement Learning for Distributed Flexible Job Shop Scheduling With Random Job Arrival
by: Yuhang Yan, et al.
Published: (2025-01-01)