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About the database ScholarIQanswers from OpenAlex & ORCID
How has Robert Heidsieck's publication output changed over time?
ScholarIQpublication output · 1987–2025
Output grew100% over the shown period — from 1 works in 1987 to 2 in 2025.
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198719892000201420202021202320242025
What are the most-cited papers on Robert Heidsieck?
ScholarIQmost cited works
Mammographic imaging with a small format CCD‐based digital cassette: Physical characteristics of a clinical system
Srinivasan Vedantham, Andrew Karellas, Sankararaman Suryanarayanan, Ilias Levis, Michel Sayag, Robert Kleehammer, Robert Heidsieck, C. J. D'Orsi
S95522064. 200049 Citations
Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency
Abdelhamid Boujarif, David W. Coit, Oualid Jouini, Zhiguo Zeng, Robert Heidsieck
Reliability Engineering & System Safety. 202419 CitationsOPEN ACCESS
Towards Hybrid Machine Learning Models in Decision Support Systems for predicting the SpareParts Reverse Flow in a Complex Supply Chain
Hamza El Garrab, Bruno Castanier, David Lemoine, Adnane Lazrak, Robert Heidsieck
S4306402512. 20204 Citations
Predicting the reverse flow of spare parts in a complex supply chain: contribution of hybrid machine learning methods in an industrial context
Hamza El Garrab, David Lemoine, Adnane Lazrak, Robert Heidsieck, Bruno Castanier
S52125040. 20234 Citations
A Deep-Learning-Based Framework to Predict the Reliability of Multicomponent Repairable Systems in a Closed-Loop Supply Chain
Abdelhamid Boujarif, David W. Coit, Oualid Jouini, Zhiguo Zeng, Robert Heidsieck
IEEE Transactions on Reliability. 20253 CitationsOPEN ACCESS
Related on ScholarIQ
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Institution
Mammographic imaging with a small format CCD‐based digital cassette: Physical characteristics of a clinical system
Paper
Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency
Paper
Towards Hybrid Machine Learning Models in Decision Support Systems for predicting the SpareParts Reverse Flow in a Complex Supply Chain
Paper
Predicting the reverse flow of spare parts in a complex supply chain: contribution of hybrid machine learning methods in an industrial context
Paper
A Deep-Learning-Based Framework to Predict the Reliability of Multicomponent Repairable Systems in a Closed-Loop Supply Chain
Paper