文章基本信息

标题：Machine Learning in Production Planning and Control: A Review of Empirical Literature
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作者：Juan Pablo Usuga Cadavid ; Samir Lamouri ; Bernard Grabot 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：13
页码：385-390
DOI：10.1016/j.ifacol.2019.11.155
语种：English
出版社：Elsevier
摘要：Proper Production Planning and Control (PPC) is capital to have an edge over competitors, reduce costs and respect delivery dates. With regard to PPC, Machine Learning (ML) provides new opportunities to make intelligent decisions based on data. Therefore, this paper provides an initial systematic review of publications on ML applied in PPC. The research objective of this study is to identify standard activities as well as techniques to apply ML in PPC. Additionally, the commonly used data sources in literature to implement a ML-aided PPC are identified. Finally, results are analyzed and gaps leading to further research are highlighted.
关键词：KeywordsMachine LearningArtificial IntelligenceIndustry 4.0Smart ManufacturingProduction PlanningControl