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

Sebastian Lang; Michael Schenk; Tobias Reggelin

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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

机译：面向生产和物流中短期操作性计划任务的基于学习和知识的人工智能方法：研究思想和框架

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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.

机译：在日益数字化的推动下，专家们估计生产系统的计划和操作将发生重大变化。趋势表明从集中控制和固定的互连生产资源向由自我管理的网络物理系统组成的分散生产转变。本文介绍了短期经营生产和物流计划所面临的挑战以及当前方法的局限性。在进一步的课程中，本文讨论了人工神经网络和模糊逻辑在解决生产和物流中短期操作计划任务方面的应用潜力。本文以研究框架作为结束，该框架概述了我们未来的步骤。

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《IFAC PapersOnLine》 |2019年第13期|共6页
作者
Sebastian Lang; Michael Schenk; Tobias Reggelin;
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正文语种
中图分类自动化技术、计算机技术;
关键词
Artificial IntelligenceNeural NetworksFuzzy LogicProductionLogistics;

机译：人工智能神经网络模糊逻辑生产物流;
入库时间 2022-08-18 18:21:01

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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

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