A Research Agenda for Artificial Intelligence in the Field of Flexible Production Systems

• URL: http://arxiv.org/abs/2112.15484v1
• Date: Fri, 31 Dec 2021 14:38:31 GMT
• Title: A Research Agenda for Artificial Intelligence in the Field of Flexible Production Systems
• Authors: Aljosha K\"ocher and Ren\'e Heesch and Niklas Widulle and Anna Nordhausen and Julian Putzke and Alexander Windmann and Sven Vagt and Oliver Niggemann
• Abstract summary: Production companies face problems when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches aiming to encapsulate production functions in the sense of services have shown to be promising in order to increase flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding production plans based on provided functionalities for a set of requirements, especially when there is no direct (i.e., syntactic) match between demanded and provided functions.
• Score: 53.47496941841855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Production companies face problems when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches aiming to encapsulate production functions in the sense of services have shown to be promising in order to increase flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding production plans based on provided functionalities for a set of requirements, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. In such cases it can become complicated to find those provided functions that can be arranged into a plan satisfying the demand. While there is a variety of different approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.

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