TY - CHAP
T1 - Agent-Oriented Smart Factory (AOSF)
T2 - An MAS Based Framework for SMEs Under Industry 4.0
AU - Ud Din, Fareed
AU - Henskens, Frans
AU - Paul, David
AU - Wallis, Mark
PY - 2019
Y1 - 2019
N2 - For the concept of Industry 4.0 to come true, a mature amalgamation of allied technologies is obligatory, i.e. Internet of Things (IoT), Big Data analytics, Mobile Computing, Multi-Agent Systems (MAS) and Cloud Computing. With the emergence of the fourth industrial revolution, proliferation in the field of Cyber-Physical Systems (CPS) and Smart Factory gave a boost to recent research in this dimension. Despite many autonomous frameworks contributed in this area, there are very few widely acceptable implementation frameworks, particularly for Small to Medium Size Enterprises (SMEs) under the umbrella of Industry 4.0. This paper presents an Agent-Oriented Smart Factory (AOSF) framework, integrating the whole supply chain (SC), from supplier-end to customer-end. The AOSF framework presents an elegant mediating mechanism between multiple agents to increase robustness in decision making at the base level. Classification of agents, negotiation mechanism and few results from a test case are presented.
AB - For the concept of Industry 4.0 to come true, a mature amalgamation of allied technologies is obligatory, i.e. Internet of Things (IoT), Big Data analytics, Mobile Computing, Multi-Agent Systems (MAS) and Cloud Computing. With the emergence of the fourth industrial revolution, proliferation in the field of Cyber-Physical Systems (CPS) and Smart Factory gave a boost to recent research in this dimension. Despite many autonomous frameworks contributed in this area, there are very few widely acceptable implementation frameworks, particularly for Small to Medium Size Enterprises (SMEs) under the umbrella of Industry 4.0. This paper presents an Agent-Oriented Smart Factory (AOSF) framework, integrating the whole supply chain (SC), from supplier-end to customer-end. The AOSF framework presents an elegant mediating mechanism between multiple agents to increase robustness in decision making at the base level. Classification of agents, negotiation mechanism and few results from a test case are presented.
UR - https://doi.org/10.1007/978-3-319-92031-3
U2 - 10.1007/978-3-319-92031-3_5
DO - 10.1007/978-3-319-92031-3_5
M3 - Chapter
SN - 9783319920313
SN - 9783319920306
T3 - Smart Innovation, Systems and Technologies
SP - 44
EP - 54
BT - Agents and Multi-Agent Systems: Technologies and Applications 2018
A2 - Jezic, Gordan
A2 - Jessica Chen-Burger, Yun-Heh
A2 - J Howlett, Robert
A2 - C Jain, Lakhmi
A2 - Vlacic, Ljubo
A2 - Sperka, Roman
PB - Springer
CY - Cham, Switzerland
ER -