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A MULTI-AGENT REINFORCEMENT LEARNING FRAMEWORK FOR INTELLIGENT MANUFACTURING WITH AUTONOMOUS MOBILE ROBOTS
2021
Proceedings of the Design Society
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This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. ...
In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. ...
Multi-agent systems for job scheduling aim to complete parallel and sequential jobs with limited manufacturing resources through effective shop floor control. ...
doi:10.1017/pds.2021.17
fatcat:bjoy4jmoaffwvdundqzi3b4e5e
Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization
2022
Sustainability
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In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. ...
In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. ...
Discussion and Conclusions In this paper, we proposed a deep reinforcement learning framework with the PPO algorithm to address the dynamic scheduling problem of the job-shop manufacturing system. ...
doi:10.3390/su14095177
fatcat:zquv2un2xbhpzj3hj66r2cb3l4
Improving Multi-agent Based Scheduling by Neurodynamic Programming
[chapter]
2003
Lecture Notes in Computer Science
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., a job-shop scheduling, are classical NP-hard problems. In the paper a two-level adaptation method is proposed to solve the scheduling problem in a dynamically changing and uncertain environment. ...
It is capable of solving the job-shop scheduling efficiently and with great fault tolerance. ...
Before we continue our investigation on job-shop scheduling, let us give a short review on multi-agent systems in general and in manufacturing. ...
doi:10.1007/978-3-540-45185-3_11
fatcat:vlv5l4pi3rf7bndijzap2o26aq
Dynamic job-shop scheduling using reinforcement learning agents
2000
Robotics and Autonomous Systems
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The agent selects the most appropriate priority rule according to the shop conditions in real time, while simulated environment performs scheduling activities using the rule selected by the agent. ...
The agent is trained by an improved reinforcement learning algorithm through the learning stage and then it successively makes decisions to schedule the operations. : S 0 9 2 1 -8 8 9 0 ( 0 0 ) 0 0 0 8 ...
On the other hand, the relation between jobs and shop floor is not so static that the systems proposed in that manner are not suitable in real life. ...
doi:10.1016/s0921-8890(00)00087-7
fatcat:bcwgykox2jbclpgoyclat6bmky
A job-shop scheduling method based on multi-agent immune algorithm
2009
2009 Chinese Control and Decision Conference
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Combining the intelligent ant and reinforcement learning, an on-line job-shop scheduling model based on the adaptive agent was proposed. ...
In the process of learning, the intelligent ant made decision according to the past rewards and an immediate reward. ...
Job-shop Scheduling Method based on Adaptive Agent In reinforcement learning, agent takes action acting on the environment in a certain state, and then the environment gives the estimate for action. ...
doi:10.1109/ccdc.2009.5191798
fatcat:fhe2kpyerne7rcqmong5w5lh34
Application of Machine Learning and Rule Scheduling in a Job-Shop Production Control System
2021
International Journal of Simulation Modelling
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Then, deep reinforcement learning was introduced to job-shop production control system to transform the dynamic job-shop production control problem. ...
The desired control objectives are not easily achieved for job-shop production control problems with dynamic changes. ...
In this paper, deep reinforcement learning is introduced to the job-shop production control system. ...
doi:10.2507/ijsimm20-2-co10
fatcat:anlk7inkpvbexi67jd7m6wqv4e
Manufacturing Dispatching using Reinforcement and Transfer Learning
[article]
2019
arXiv
pre-print
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Using reinforcement learning (RL), we propose a new design to formulate the shop floor state as a 2-D matrix, incorporate job slack time into state representation, and design lateness and tardiness rewards ...
This requires efficient dispatching that can work in dynamic and stochastic environments, meaning it allows for quick response to new orders received and can work over a disparate set of shop floor settings ...
T D(λ) based reinforcement learning was used for manufacturing job shop scheduling to improve resource utilization [25] . ...
arXiv:1910.02035v1
fatcat:eknaz6pdqff2te6jcysq542654
Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation
2021
Production Engineering
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Integrated in manufacturing control, turbulence mitigation increases manufacturing resilience and strengthens the supply network's resilience. ...
Nowadays, raw data is available in large quantities. An obstacle to manufacturing control is the short-term handling of events induced by customers and suppliers. ...
In contrast, reinforcement learning does not require (labeled) data, but an environment that enables learning from interaction with it [36, 37] . ...
doi:10.1007/s11740-021-01036-4
fatcat:za7efbaplvh5joasnh72f2fgkq
Reinforcement Learning on Job Shop Scheduling Problems Using Graph Networks
[article]
2020
arXiv
pre-print
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This paper presents a novel approach for job shop scheduling problems using deep reinforcement learning. ...
Furthermore, we cast the JSSP as a distributed optimization problem in which learning agents are individually assigned to resources which allows for higher flexibility with respect to changing production ...
GNNs on reinforcement learning in scheduling problems [41] . ...
arXiv:2009.03836v1
fatcat:ggwyztrkvzbxhemiuoqr6hjzmi
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems
2020
IEEE Access
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INDEX TERMS Job shop scheduling problem (JSSP), deep reinforcement learning, actor-critic network, parallel training. This work is licensed under a Creative Commons Attribution 4.0 License. ...
In the past decades, many optimization methods have been devised and applied to job shop scheduling problem (JSSP) to find the optimal solution. ...
Reinforcement learning (RL) is a type of machine learning concerned with how agents should take actions in an environment so as to maximize the future reward. ...
doi:10.1109/access.2020.2987820
fatcat:eaxcuqrwe5cblbsav3vbhbdb7y
Foresighted digital twin for situational agent selection in production control
2021
Procedia CIRP
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In today's business environment, the trend towards more product variety and customization is unbroken. ...
Abstract As intelligent Data Acquisition and Analysis in Manufacturing nears its apex, a new era of Digital Twins is dawning. ...
Acknowledgements This research work was undertaken in the context of the DIGIMAN4.0 project ("DIGItal MANufacturing Technologies for Zero-defect Industry 4.0 Production'', http://www.digiman4-0.mek.dtu.dk ...
doi:10.1016/j.procir.2021.03.005
fatcat:5tx2v4kvzbg3vmuon3i7zntos4
Hybrid Intelligent Algorithm for Flexible Job-Shop Scheduling Problem under Uncertainty
[chapter]
2011
Advances in Reinforcement Learning
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In a job-shop DEDS (Discrete Event Dynamic System), the JSP can be www.intechopen.com Advances in Reinforcement Learning 362 solved by its parsing model and method, such as Petri net. ...
The manufacturing resources in the job-shop is enough with all necessary machine for mold processing, which include lathe, milling machine, grinding machine, numeral control machine, process center, electric ...
Hybrid Intelligent Algorithm for Flexible Job-Shop Scheduling Problem under Uncertainty, Advances in Reinforcement Learning, Prof. ...
doi:10.5772/13195
fatcat:6rfzylvckvghzdhupi4keytpz4
Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops
2022
Sustainability
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in smart manufacturing. ...
The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. ...
Acknowledgments: The authors thanks Shanghai Key Laboratory of intelligent manufacturing and robotics of Shanghai University for its support for this study. ...
doi:10.3390/su14095340
fatcat:3jdonvohara47inehlfk7vy4ca
Intelligent Scheduling with Reinforcement Learning
2021
Applied Sciences
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It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. ...
In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. ...
shop problem), machine learning and reinforcement learning; Section 3 characterizes the formulation of the job shop scheduling problem as a reinforcement learning problem; Section 4 presents the complete ...
doi:10.3390/app11083710
fatcat:bkmanj4ycnc5limjiilazthsli
Reinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints
2020
Production Engineering
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in a complex job shop with strict time constraints. ...
The simulation represents the characteristics of complex job shops typically found in semiconductor manufacturing. ...
All in all, wafer fabs belong to the category of complex job shops as a distinct job shop type with the features described above [23] . ...
doi:10.1007/s11740-020-00967-8
fatcat:qw4ong4snncnfdylytrfcbrtbe
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