首页    期刊浏览 2024年10月05日 星期六
登录注册

文章基本信息

  • 标题:An approach for AI-based forecasting of maintenance orders for MRO scheduling
  • 本地全文:下载
  • 作者:Florian Öhlinger ; Lisa Greimel ; Robert Glawar
  • 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
国家哲学社会科学文献中心版权所有