| DC Field | Value | Language |
|---|
| dc.contributor.author | Balasanov, Y. | - |
| dc.contributor.author | Tselishchev, M. | - |
| dc.date.accessioned | 2019-03-15T11:03:42Z | - |
| dc.date.available | 2019-03-15T11:03:42Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Balasanov, Y. Application of reinforcement learning to revenue management / Y. Balasanov, M. Tselishchev // BIG DATA and Advanced Analytics = BIG DATA и анализ высокого уровня : сборник материалов V Международной научно-практической конференции, Минск, 13–14 марта 2019 г. В 2 ч. Ч. 1 / Белорусский государственный университет информатики и радиоэлектроники; редкол. : В. А. Богуш [и др.]. – Минск, 2019. – С. 19 – 27. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/34700 | - |
| dc.description.abstract | Problem of revenue management.Every seller of a product or service has to make some fundamental
decisions:a child making a lemonade booth: when to have the sale, how much to ask for each cup and when to drop
the price to finish the sale at the end of the day;a homeowner selling a house: when to list it, what price to ask, which
offer to accept, when to lower the price or unlist the house if necessary; an eBay seller: what is the duration of the
auction, what is the starting price, all these are variations of famous optimization problem known under names like:
the secretary problem, the marriage problem, the sultan's dowry problem, the fussy suitor problem, etc. The key issue
is optimization under uncertainty of the future. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | reinforcement learning problem | ru_RU |
| dc.subject | revenue management | ru_RU |
| dc.subject | Q-learning | ru_RU |
| dc.title | Application of reinforcement learning to revenue management | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | BIG DATA and Advanced Analytics = BIG DATA и анализ высокого уровня : материалы конференции (2019)
|
