A Research Agenda for AI Planning in the Field of Flexible Production Systems

TL;DR

This paper explores AI planning techniques, including symbolic AI and machine learning, to enhance flexibility in manufacturing production systems, addressing challenges in adapting to fluctuating demands and changing requirements.

Contribution

It identifies specific requirements for flexible production environments and proposes a research agenda comparing AI approaches to meet these needs.

Findings

Current AI planning methods can be adapted for flexible production

Symbolic AI and machine learning approaches offer complementary solutions

A research agenda guides future development in AI for manufacturing flexibility

Abstract

Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of 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. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. 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.