文章基本信息

标题：An approach for AI-based forecasting of maintenance orders for MRO scheduling
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作者：Florian Öhlinger ; Lisa Greimel ; Robert Glawar 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：10
页码：2312-2317
DOI：10.1016/j.ifacol.2022.10.053
语种：English
出版社：Elsevier
摘要：Maintenance orders are difficult to plan and require a high degree of flexibility, because both the extent of the activity ("What needs to be done?"), the scheduling ("When is the repair to be carried out?") and spatial restrictions ("Where is the repair to be carried out?") are largely unknown at the beginning of an order. All this results in a wide-spread reactive maintenance coordination in the industry instead of an efficient proactive maintenance planning of the diverse process. This not only leads to losses in the form of waiting and downtimes, but the lack of transparency both in the utilization situation and about the status of each order leads to delivery date difficulties and wasted resources. In order to decisively improve the status quo, it is indispensable to improve the accuracy of information on the above-mentioned questions as soon as possible after receiving the order. In this paper, an approach for the application of AI in MRO scheduling is presented including the line of research which needs to be done to enable a holistic planning optimization with decision support.
关键词：maintenance;repair;overhaul;artificial intelligence;scheduling;knowledge-based systems