Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination

被引:821
作者
Wang, Shiyong [1 ]
Wan, Jiafu [1 ]
Zhang, Daqiang [2 ]
Li, Di [1 ]
Zhang, Chunhua [1 ]
机构
[1] S China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] Tongji Univ, Sch Software Engn, Shanghai 200092, Peoples R China
基金
美国国家科学基金会;
关键词
Industry; 4.0; Smart factory; Cyber-physical system; Multi-agent system; Deadlock prevention; CYBER-PHYSICAL SYSTEMS; ARCHITECTURE; RESOURCE; INTERNET;
D O I
10.1016/j.comnet.2015.12.017
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The proliferation of cyber-physical systems introduces the fourth stage of industrialization, commonly known as Industry 4.0. The vertical integration of various components inside a factory to implement a flexible and reconfigurable manufacturing system, i.e., smart factory, is one of the key features of Industry 4.0. In this paper, we present a smart factory framework that incorporates industrial network, cloud, and supervisory control terminals with smart shop-floor objects such as machines, conveyers, and products. Then, we provide a classification of the smart objects into various types of agents and define a coordinator in the cloud. The autonomous decision and distributed cooperation between agents lead to high flexibility. Moreover, this kind of self-organized system leverages the feedback and coordination by the central coordinator in order to achieve high efficiency. Thus, the smart factory is characterized by a self-organized multi-agent system assisted with big data based feedback and coordination. Based on this model, we propose an intelligent negotiation mechanism for agents to cooperate with each other. Furthermore, the study illustrates that complementary strategies can be designed to prevent deadlocks by improving the agents' decision making and the coordinator's behavior. The simulation results assess the effectiveness of the proposed negotiation mechanism and deadlock prevention strategies. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:158 / 168
页数:11
相关论文
共 25 条
[1]   Adaptation to Climate Change in Industry: Improving Resource Efficiency through Sustainable Production Applications [J].
Alkaya, Emrah ;
Bogurcu, Merve ;
Ulutas, Ferda ;
Demirer, Goeksel Niyazi .
WATER ENVIRONMENT RESEARCH, 2015, 87 (01) :14-25
[2]  
[Anonymous], INT C EMB UB COMP EU
[3]  
[Anonymous], 2013, Recommendations for implementing the strategic initiative INDUSTRIE 4.0
[4]  
[Anonymous], 2014, IND INT CONS GLOB NO
[5]   Towards the integration of flexible manufacturing system scheduling [J].
Balogun, OO ;
Popplewell, K .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1999, 37 (15) :3399-3428
[6]   Data Mining for the Internet of Things: Literature Review and Challenges [J].
Chen, Feng ;
Deng, Pan ;
Wan, Jiafu ;
Zhang, Daqiang ;
Vasilakos, Athanasios V. ;
Rong, Xiaohui .
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015,
[7]   Big Data: A Survey [J].
Chen, Min ;
Mao, Shiwen ;
Liu, Yunhao .
MOBILE NETWORKS & APPLICATIONS, 2014, 19 (02) :171-209
[8]   Towards Socio-Cyber-Physical Systems in Production Networks [J].
Frazzon, Enzo Morosini ;
Hartmann, Jens ;
Makuschewitz, Thomas ;
Scholz-Reiter, Bernd .
FORTY SIXTH CIRP CONFERENCE ON MANUFACTURING SYSTEMS 2013, 2013, 7 :49-54
[9]   Security of the Internet of Things: perspectives and challenges [J].
Jing, Qi ;
Vasilakos, Athanasios V. ;
Wan, Jiafu ;
Lu, Jingwei ;
Qiu, Dechao .
WIRELESS NETWORKS, 2014, 20 (08) :2481-2501
[10]   Agent-based distributed manufacturing control: A state-of-the-art survey [J].
Leitao, Paulo .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2009, 22 (07) :979-991