{"updated":"2025-01-19T09:57:32.562032+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233854","sets":["1164:4061:11479:11599"]},"path":["11599"],"owner":"44499","recid":"233854","title":["量子アニーリングとブラックボックス最適化を用いた倉庫内業務最適化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-05-02"},"_buckets":{"deposit":"23fa2251-d152-4a67-bccc-0f5a7123c875"},"_deposit":{"id":"233854","pid":{"type":"depid","value":"233854","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"量子アニーリングとブラックボックス最適化を用いた倉庫内業務最適化","author_link":["636146","636147","636148","636145","636149","636150"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"量子アニーリングとブラックボックス最適化を用いた倉庫内業務最適化"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"行動認識","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-05-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科"},{"subitem_text_value":"名古屋大学大学院工学研究科／名古屋大学未来社会創造機構"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate 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魁利"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"瀧上, 昂希"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"片山, 晋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浦野, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"米澤, 拓郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"河口, 信夫"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11838947","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8698","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"サプライチェーンの拡大に伴い，モノの中継地点である物流倉庫のさらなる効率化が求められている．しかし物流倉庫は休みなく運用されるため，現実空間における運用方法・環境の変更と，その定量的な評価は容易ではなく，センシングによる倉庫環境デジタル化と仮想環境の最適化が不可欠である．最適化の一種に物流・経済・交通などの多様な分野で応用されている組合せ最適化がある．近年，組合せ最適化問題に特化した量子アニーリングマシンと呼ばれるハードウェアが登場し，現実に存在する多様な組合せ最適化問題を高速に解く手段として注目を集めている．一般に組合せ最適化問題を解くためには，対象とするドメインの専門知識に基づいた独自の数理モデルを構築し，最適化を行う．しかし，数理モデルの構築は容易ではなく，独自の数理モデルに基づいた最適解は，実環境やシミュレータで評価を行うまで有効性が保証されない．そのため，実環境やシミュレータに基づいた最適化を行う必要がある．本研究では量子アニーリングとシミュレータを用いた実在する物流倉庫の入荷場を対象とした業務とシフトを割り当てる組合せ最適化を行う．具体的には倉庫内シミュレータと Factorization Machine の学習による数理モデル化，そして量子アニーリングによる最適化を相互に繰り返し，最適化を行う．本手法の最適化の結果，実際の倉庫での業務とシフトと比較してリードタイムを最大 37.37%，残留荷物数を最大 95.47%，合計労働時間を最大 14.29% 削減した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ユビキタスコンピューティングシステム（UBI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-05-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicVolumeNumber":"2024-UBI-82"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233854,"created":"2025-01-19T01:35:28.418835+00:00"}