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.
翻译:生产公司在迅速调整其生产控制以适应变化不定的需求或不断变化的要求时面临问题。旨在将生产功能包含在服务意义上的管制办法已证明很有希望,以便提高网络物理生产系统的灵活性。但是,这种办法的现有挑战是如何根据一系列要求的提供功能来寻找生产计划,特别是当需求功能和提供功能之间没有直接(即合成)的匹配时。在这种情况下,发现所提供的功能可以安排在满足需求的计划中时可能会变得复杂。虽然对生产规划有各种不同的方法,但灵活生产提出了现有研究没有涵盖的具体要求。在此贡献中,我们首先捕捉灵活生产环境的这些要求。随后,将概述目前可用于克服上述挑战的人工智能方法。讨论象征性的人工智能规划和基于机器学习的方法,最终与要求进行比较。根据这种比较,将得出一项研究议程。