{"updated":"2025-01-19T16:20:36.390620+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215052","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"215052","title":["並列機械スケジューリングの最適化のための機械学習を用いた作業時間推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"9c92192b-6848-4b0d-a157-51a00b5cbd61"},"_deposit":{"id":"215052","pid":{"type":"depid","value":"215052","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"並列機械スケジューリングの最適化のための機械学習を用いた作業時間推定","author_link":["553502","553503"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"並列機械スケジューリングの最適化のための機械学習を用いた作業時間推定"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"長岡技科大"},{"subitem_text_value":"長岡技科大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/215052/files/IPSJ-Z83-5R-09.pdf","label":"IPSJ-Z83-5R-09.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-5R-09.pdf","filesize":[{"value":"127.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"d944db7c-9b0c-4069-9748-ad5baf9a9590","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山城, 広周"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"野中, 尋史"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"既存研究では，作業時間が既知である場合や，単純な分布に従うと仮定している．しかし，実際の工場における作業時間は，未知である場合が多く，複雑な分布に従っている可能性が高い．そこで，本研究では，機械学習モデルを用いて作業時間を推定することを考える．本研究では，実際の工場データにおいて作業時間が複雑な分布に従う場合に，機械学習モデルを用いて作業時間を推定し，スケジューリング最適化を行うシステムを提案した．研究協力企業より提供された製造サンプルごとにまとめられた作業時間が既知である工程情報を用いて，提案手法を評価した．MAPEを用いて機械学習モデルの評価を行った結果，LightGBMが22.5%で最も良かった．推定作業時間を用いて，並列機械スケジューリングの最適化を行った結果，makespanの平均短縮率は，29.5%であった．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"508","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"507","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:15:50.187221+00:00","id":215052,"links":{}}