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AI、機械学習、数理最適化で分析して、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（AI、機械学習、数理最適化、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, 11, 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
Nagahara, S., et al.,Toward data-driven production simulation modeling: dispatching rule identification by machine learning techniques. Proceedia CIRP 81, 222–227 (2019)
https://doi.org/10.1016/j.procir.2019.03.039
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
Nishi, K., et al., Evolvable Motion-planning Method using Deep Reinforcement Learning, 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021,
https://doi.org/10.1109/ICRA48506.2021.9561602
Umeda, S., et al., Advanced Process Control Using Virtual Metrology to Cope With Etcher Condition Change, IEEE Transactions on Semiconductor Manufacturing( Volume: 32, Issue: 4, Nov. 2019), pp. 423-427,
https://doi.org/10.1109/TSM.2019.2938546
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
