製造・流通・小売り分野向けのスマート生産システムの開発を行っています。流通データ、製品データ、製造・配送現場データ等を数理最適化、生成AI、機械学習により、自律的に最適な業務オペレーションを実現するシステムを開発します。
製造・流通分野のデータアナリティクス・計画最適化（数理最適化、生成AI、機械学習、サイバーフィジカルシステム、シミュレーション）
製造現場のデジタル化（IoT、設備センシング、製造現場モニタリング)
Publishing Academic Papers:
Hosoda, J., et al., Location routing problem with delivery modes, International Journal of Logistics Systems and Management, 2020, 36(3), pp. 370-384.
https://doi.org/10.1504/IJLSM.2020.108696
Seto, A., et al., Hierarchical Clustering-Based Network Design Algorithm for Many-To-Many Hub Location Routing Problem, 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021, pp. 89-94.
https://doi.org/10.1109/CASE49439.2021.9551550
Hosoda, J., et al., Recent Research on Variants of the Location Routing Problem, Journal of Japan Industrial Management Association, 2022, 73(2E), pp. 75-91,
https://doi.org/10.11221/jima.73.75
Hosoda, J., et al., Location, transshipment and routing: An adaptive transportation network integrating long-haul and local vehicle routing, EURO Journal on Transportation and Logistics, 2022, Volume 11, Article 100091.
https://doi.org/10.1016/j.ejtl.2022.100091
Kiuchi, A., et al., Bayesian Optimization Algorithm with Agent-based Supply Chain Simulator for Multi-echelon Inventory Management, 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 2020, pp. 418-425,
https://doi.org/10.1109/CASE48305.2020.9216792
Kiuchi, A., Recurrent Neural Network Based Reinforcement Learning for Inventory Control with Agent-based Supply Chain Simulator, 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024, pp.1903-1909
https://doi.org/10.1109/CASE59546.2024.10711446
Nagahara, S., et al.,Toward data-driven production simulation modeling: dispatching rule identification by machine learning techniques. Procedia CIRP 81, 222–227 (2019)
https://doi.org/10.1016/j.procir.2019.03.039
Nagahara, S., et al., Toward Data-Driven and Multi-Scale Modeling for Material Flow Simulation: Characteristic Analysis of Modeling Methods. Applied Artificial Intelligence, Volume 38, Issue 1, Article 2367840 (2024)
https://doi.org/10.1080/08839514.2024.2367840
Takeda, H., et al., Welding Sequence and Robot Path Optimization for Spot Welding Robots to Prevent Deformation of Sheet Metal Parts, 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024,
https://doi.org/10.1109/CASE59546.2024.10711537
Daisuke T., et al., Managing Fluctuations in Production via an Optimal Portfolio of Assembly Line Configurations, Procedia CIRP,
Volume 130, 2024, Pages 1372-1377.
https://doi.org/10.1016/j.procir.2024.10.254
Tsutsumi, D., et al., Novel heuristic approach to integrating task sequencing and production system configuration, Procedia CIRP 107, 28-33(2022)
https://doi.org/10.1016/j.procir.2022.04.005
Nakano, T., et al., Manufacturing Line Design Configuration with Optimized Resource Groups, 2021 IEEE 16th International Conference on Automation Science and Engineering (CASE), 2021,
https://doi.org/10.1109/CASE49439.2021.9551650
2020年度(第40回)精密工学会技術賞
生産環境変動に迅速に対応できるロボット生産ラインの一貫自動設計技術の開発
https://www.hitachi.co.jp/rd/about/awards/2020.html
配送計画最適化ソリューション
https://www.hitachi.co.jp/New/cnews/month/2019/02/0228.html
サプライチェーン最適化ソリューション
https://www.hitachi.co.jp/products/it/industry/solution/dsc/dsc_sc.html
https://www.hitachi.co.jp/New/cnews/month/2020/07/0714.pdf
https://www.hitachi.co.jp/New/cnews/month/2022/12/1206a.html
https://www.hitachi.co.jp/New/cnews/month/2024/08/0826.html
サプライチェーンプラットフォーム
https://www.hitachi.co.jp/New/cnews/month/2024/11/1112a.html
