Optimization of schedule for distribution of dosimeter sets by the IAEA/WHO using machine learning
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| dc.contributor.author | Vedernikova, Anastasiia
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| dc.date.accessioned | 2024-02-14T17:02:27Z | |
| dc.date.available | 2024-02-14T17:02:27Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Vedernikova, Anastasiia. Optimization of schedule for distribution of dosimeter sets by the IAEA/WHO using machine learning / Vedernikova, Anastasiia; Supervisor: Yaroslav Pynda, Tomislav Bokulic; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2019. – 64 p. | uk |
| dc.identifier.uri | https://er.ucu.edu.ua/handle/1/4486 | |
| dc.language.iso | en | uk |
| dc.title | Optimization of schedule for distribution of dosimeter sets by the IAEA/WHO using machine learning | uk |
| dc.type | Preprint | uk |
| dc.status | Публікується вперше | uk |
| dc.description.abstracten | IAEA dosimetry laboratory (DOL) uses almost the same schedule for sending RPLD sets to hospitals around the world each year. Hospitals irradiate sets and send them back to the DOL for further analysis. The workload intensity of laboratory mostly depends on number of sets it receives each month. The goal of this project is to cre- ate more balanced schedule of irradiation windows, by minimizing the difference between received number of sets each month. The project consists of three main steps: forecasting waiting time, forecasting number of sets and scheduling. As a re- sult, predictions for waiting time created by LSTM and ARIMA, together with pre- dictions for number of sets created by Exponential Smoothing were used to generate more balanced schedule of irradiation windows using Linear Programming. New schedule satisfies all constraints and can be used next year for IAEA/WHO postal dose quality audits. | uk |
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