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Towards Learning- and Knowledge-Based Methods of Artificial Intelligence for Short-Term Operative Planning Tasks in Production and Logistics: Research Idea and Framework

By S. Lang, M. Schenk and T. Reggelin

Abstract

Driven by the increasing digitalization, experts estimate a major change concerning the planning and operation of production systems. The trends indicate a shift from centrally controlled and fixed interlinked production resources to a decentralized production consisting of self-managing cyber-physical systems. This article describe the resulting challenges for the short-term operative production and logistics planning as well as the limitations of current methods. In the further course, the article discusses application potentials of artificial neural networks and fuzzy logic to tackle short-term operative planning tasks in production and logistics. The article concludes with a research framework, which outlines our future steps

Year: 2019
DOI identifier: 10.1016/j.ifacol.2019.11.618
OAI identifier: oai:fraunhofer.de:N-585451
Provided by: Fraunhofer-ePrints
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