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pro vyhledávání: '"Akcay, Alp"'
We consider a physical asset consisting of complex systems, where the systems may require upgrades during the lifetime of the asset. In practice, the asset owner and system supplier can make the upgrade decisions together, requiring a decision suppor
Externí odkaz:
http://arxiv.org/abs/2202.12128
Autor:
da Costa, Paulo, Verleijsdonk, Peter, Voorberg, Simon, Akcay, Alp, Kapodistria, Stella, van Jaarsveld, Willem, Zhang, Yingqian
Downtime of industrial assets such as wind turbines and medical imaging devices comes at a sharp cost. To avoid such downtime costs, companies seek to initiate maintenance just before failure. Unfortunately, this is challenging for the following two
Externí odkaz:
http://arxiv.org/abs/2105.15119
Spare parts recommendation for corrective maintenance of capital goods considering demand dependency
Publikováno v:
In European Journal of Operational Research April 2024
Publikováno v:
In Computers & Industrial Engineering January 2024 187
Publikováno v:
In International Journal of Production Economics January 2024 267
In biopharmaceutical manufacturing, fermentation processes play a critical role in productivity and profit. A fermentation process uses living cells with complex biological mechanisms, leading to high variability in the process outputs, namely, the p
Externí odkaz:
http://arxiv.org/abs/2101.03735
In this paper, we study a slate bandit problem where the function that determines the slate-level reward is non-separable: the optimal value of the function cannot be determined by learning the optimal action for each slot. We are mainly concerned wi
Externí odkaz:
http://arxiv.org/abs/2004.09957
Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to
Externí odkaz:
http://arxiv.org/abs/2004.01608
Akademický článek
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In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target)
Externí odkaz:
http://arxiv.org/abs/1907.07480