Determining the optimal level of autonomy in cyber-physical production systems

Traditional production systems are enhanced by cyber-physical systems (CPSs) and Internet of Things. As kind of next generation systems, those cyber-physical production systems (CPPSs) are able to raise the level of autonomy of its production components. To find the optimal degree of autonomy in a given context, a research approach is formulated using a simulation concept. Based on requirements and assumptions, a cyber-physical market is modeled and qualitative hypotheses are formulated, which will be verified with the help of the CPPS of a hybrid simulation environment. Big Data Analytics can be used to extract influence factors which explain the optimal degree of autonomy.

[1]  Christoph Schlieder,et al.  Winspect: a case study for wearable computing-supported inspection tasks , 2001, Proceedings Fifth International Symposium on Wearable Computers.

[2]  Detlef Zuehlke,et al.  SmartFactory – A Vision becomes Reality , 2009 .

[3]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[4]  SO KUTC..  Optimal buffer allocation strategy for minimizing work-in- process inventory in unpaced production lines , 1997 .

[5]  Birgit Vogel-Heuser,et al.  Global Information Architecture for Industrial Automation , 2013 .

[6]  Kalevi Kyläheiko,et al.  An analytic approach to production capacity allocation and supply chain design , 2002 .

[7]  Sergey Dashkovskiy,et al.  Autonomous control methods in logistics - A mathematical perspective , 2012 .

[8]  Siddhartha Kumar Khaitan,et al.  Design Techniques and Applications of Cyberphysical Systems: A Survey , 2015, IEEE Systems Journal.

[9]  Kevin Ashton,et al.  That ‘Internet of Things’ Thing , 1999 .

[10]  Liang Gao,et al.  Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling , 2010, Comput. Oper. Res..

[11]  Wilhelm Dangelmaier,et al.  A multi-objective evolutionary approach to scheduling for evolving manufacturing systems , 2011, Evolving Systems.

[12]  Y. Guoa,et al.  Applications of particle swarm optimisation in integrated process planning and scheduling , 2008 .

[13]  Richard Bartle,et al.  Designing Virtual Worlds , 2003 .

[14]  Alois Zoitl,et al.  Capability-based planning and scheduling for adaptable manufacturing systems , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[15]  Sergey Dashkovskiy,et al.  Modeling and stability analysis of autonomously controlled production networks , 2011, Logist. Res..

[16]  Samir Chatterjee,et al.  A Design Science Research Methodology for Information Systems Research , 2008 .

[17]  Liang Gao,et al.  Integration of process planning and scheduling - A modified genetic algorithm-based approach , 2009, Comput. Oper. Res..