Smart Manufacturing Scheduling System: DQN based on Cooperative Edge Computing

被引:10
作者
Moon, Junhyung [1 ]
Jeong, Jongpil [1 ]
机构
[1] Sungkyunkwan Univ, Dept Smart Factory Convergence, Suwon, South Korea
来源
PROCEEDINGS OF THE 2021 15TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM 2021) | 2021年
关键词
Smart Manufacturing; Job shop Scheduling Problem; Deep Q-Network; Multi Access Edge Computing; Cooperative Business Process; SHOP; ALGORITHM;
D O I
10.1109/IMCOM51814.2021.9377434
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, Deep Q-Network (DQN) was adopted to solve the Job shop Scheduling Problem (JSP) in the smart factory process. On the other hand, cloud computing has sensitive issues in the manufacturing process such as communication delay time and security problems. Research on various aspects of introducing an edge computing system to replace it has been conducted. We propose cooperative scheduling among edge devices in a Multi access Edge Computing (MEC) structure for scheduling without the help of a cloud center in a smart factory edge computing environment. Moreover, efficient DQN is used for experiments based on transfer learning data, and the proposed framework is compared and analyzed with existing frameworks from the perspective of provider a smart factory service.
引用
收藏
页数:8
相关论文
共 20 条
[1]   Using Smart City Data in 5G Self-Organizing Networks [J].
Cia, Massimo Dalla ;
Mason, Federico ;
Peron, Davide ;
Chiariotti, Federico ;
Polese, Michele ;
Mahmoodi, Toktam ;
Zorzi, Michele ;
Zanella, Andrea .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (02) :645-654
[2]   An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling [J].
Ge, Hong-Wei ;
Sun, Liang ;
Liang, Yan-Chun ;
Qian, Feng .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2008, 38 (02) :358-368
[3]  
Gu SX, 2016, PR MACH LEARN RES, V48
[4]  
Hernández-Ramírez L, 2019, INT J COMB OPTIM PRO, V10, P6
[5]  
Jobshop Instance, 2015, JOBSHOP INSTANCE
[6]   Design, Implementation and Evaluation of Reinforcement Learning for an Adaptive Order Dispatching in Job Shop Manufacturing Systems [J].
Kuhnle, Andreas ;
Schaefer, Louis ;
Stricker, Nicole ;
Lanza, Gisela .
52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS), 2019, 81 :234-239
[7]   Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network [J].
Lin, Chun-Cheng ;
Deng, Der-Jiunn ;
Chih, Yen-Ling ;
Chiu, Hsin-Ting .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (07) :4276-4284
[8]   A Survey on Mobile Edge Computing: The Communication Perspective [J].
Mao, Yuyi ;
You, Changsheng ;
Zhang, Jun ;
Huang, Kaibin ;
Letaief, Khaled B. .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2017, 19 (04) :2322-2358
[9]   Human-level control through deep reinforcement learning [J].
Mnih, Volodymyr ;
Kavukcuoglu, Koray ;
Silver, David ;
Rusu, Andrei A. ;
Veness, Joel ;
Bellemare, Marc G. ;
Graves, Alex ;
Riedmiller, Martin ;
Fidjeland, Andreas K. ;
Ostrovski, Georg ;
Petersen, Stig ;
Beattie, Charles ;
Sadik, Amir ;
Antonoglou, Ioannis ;
King, Helen ;
Kumaran, Dharshan ;
Wierstra, Daan ;
Legg, Shane ;
Hassabis, Demis .
NATURE, 2015, 518 (7540) :529-533
[10]   Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules [J].
Nguyen, Su ;
Zhang, Mengjie ;
Tan, Kay Chen .
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 47 (09) :2951-2965