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A MULTI-AGENT REINFORCEMENT LEARNING FRAMEWORK FOR INTELLIGENT MANUFACTURING WITH AUTONOMOUS MOBILE ROBOTS

Akash Agrawal, Sung Jun Won, Tushar Sharma, Mayuri Deshpande, Christopher McComb
2021 Proceedings of the Design Society  
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

Ming Zhang, Yang Lu, Youxi Hu, Nasser Amaitik, Yuchun Xu
2022 Sustainability  
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]

Balázs Csanád Csáji, Botond Kádár, László Monostori
2003 Lecture Notes in Computer Science  
., 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

M.Emin Aydin, Ercan Öztemel
2000 Robotics and Autonomous Systems  
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

Xinli Xu, Ping Hao, Wanliang Wang
2009 2009 Chinese Control and Decision Conference  
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

Y. Zhao, H. Zhang
2021 International Journal of Simulation Modelling  
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]

Shuai Zheng, Chetan Gupta, Susumu Serita
2019 arXiv   pre-print
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

Dennis Bauer, Markus Böhm, Thomas Bauernhansl, Alexander Sauer
2021 Production Engineering  
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]

Mohammed Sharafath Abdul Hameed, Andreas Schwung
2020 arXiv   pre-print
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

Chien-Liang Liu, Chuan-Chin Chang, Chun-Jan Tseng
2020 IEEE Access  
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

Marvin Carl May, Leonard Overbeck, Marco Wurster, Andreas Kuhnle, Gisela Lanza
2021 Procedia CIRP  
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]

Guojun Zhang, Haiping Zhu, Chaoyong Zhang
2011 Advances in Reinforcement Learning  
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

Lilan Liu, Kai Guo, Zenggui Gao, Jiaying Li, Jiachen Sun
2022 Sustainability  
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

Bruno Cunha, Ana Madureira, Benjamim Fonseca, João Matos
2021 Applied Sciences  
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

Thomas Altenmüller, Tillmann Stüker, Bernd Waschneck, Andreas Kuhnle, Gisela Lanza
2020 Production Engineering  
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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