An Improved Ant Colony Algorithm Based Dynamic Scheduling Method in Job Shop with Parallel Machines

Zhang, J.; Yang, Jing; Qin, Wei; Li, H.; Xu, Zhewen

doi:10.4028/www.scientific.net/amr.628.304

Cited by 4 publications

References 2 publications

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Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

Qin,

2024

J Intell Manuf

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Self-organizing manufacturing network has emerged as a viable solution for adaptive manufacturing control within the mass personalization paradigm. This approach involves three critical elements: system modeling and control architecture, interoperable communication, and adaptive manufacturing control. However, current research often separates interoperable communication from adaptive manufacturing control as isolated areas of study. To address this gap, this paper introduces Knowledge Graph-enhanced Multi-Agent Reinforcement Learning (MARL) method that integrates interoperable communication via Knowledge Graphs with adaptive manufacturing control through Reinforcement Learning. We hypothesize that implicit domain knowledge obtained from historical production job allocation records can guide each agent to learn more effective scheduling policies with accelerated learning rates. This is based on the premise that machine assignment preferences effectively could reduce the Reinforcement Learning search space. Specifically, we redesign machine agents with new observation, action, reward, and cooperation mechanisms considering the preference of machines, building upon our previous MARL base model. The scheduling policies are trained under extensive simulation experiments that consider manufacturing requirements. During the training process, our approach demonstrates improved training speed compared with individual Reinforcement Learning methods under the same training hyperparameters. The obtained scheduling policies generated by our Knowledge Graph-enhanced MARL also outperform both individual Reinforcement Learning methods and heuristic rules under dynamic manufacturing settings.

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Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

Qin,

2024

J Intell Manuf

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Dynamic production scheduling towards self-organizing mass personalization: A multi-agent dueling deep reinforcement learning approach

Qin

Johnson

2023

Journal of Manufacturing Systems

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Improved Ant Colony Algorithms for Eliminating Stagnation and Local Optimum Problem — A Survey

Amuthan¹,

Thilak²

2017

2017 International Conference on Technical Advancements in Computers and Communications (ICTACC)

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An Improved Ant Colony Algorithm Based Dynamic Scheduling Method in Job Shop with Parallel Machines

Cited by 4 publications

References 2 publications

Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

Dynamic production scheduling towards self-organizing mass personalization: A multi-agent dueling deep reinforcement learning approach

Improved Ant Colony Algorithms for Eliminating Stagnation and Local Optimum Problem — A Survey

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