Professor at Hiroshima University , Japan.
Professor Tomohiro Hayashida is a distinguished scholar in decision-making, machine learning, and optimization, currently serving at Hiroshima University. After earning his Master’s and Ph.D. in Engineering from the same institution, he joined the university as a Research Associate in 2006 and steadily rose to Full Professor by 2024. With over 80 academic publications, Prof. Hayashida has led competitive national research grants and worked on practical innovations in transportation and scheduling algorithms. His interdisciplinary collaborations span across academia and industry, reflecting his commitment to both theoretical advancements and real-world applications. His citation record, leadership in JSPS-funded projects, and role in applied AI solutions exemplify his deep impact in computational engineering and operations research.
🌍 Professional Profile:
Scopus
🏆 Suitability for the Best Researcher Award :
Prof. Tomohiro Hayashida exemplifies the qualities deserving of the Best Researcher Award. He has produced over 50 peer-reviewed journal papers, many in top-tier SCI/Scopus-indexed journals. As Principal Investigator of multiple JSPS KAKEN-funded projects, including those in evolutionary computing and dynamic systems, he has shown consistent research leadership. His collaborative work with industry, such as optimizing dispatch algorithms with SmartRyde Inc., demonstrates strong translational research. With an h-index around 11–12 and 442+ citations, he balances scholarly excellence and societal impact. His active role in multi-disciplinary collaborations—both domestic and international—further reinforces his stature as an innovative and impactful researcher, making him highly suitable for the Best Researcher Award.
🎓 Education :
Prof. Hayashida received his entire higher education from Hiroshima University. He completed his Master’s degree in Engineering in 2006 and subsequently pursued and earned his Ph.D. in Engineering. His academic training focused on optimization theory, intelligent systems, and operations research, equipping him with a robust foundation in both theoretical and applied research. The university’s focus on computational intelligence and engineering sciences helped shape his research vision early in his career. His seamless transition from student to researcher within the same academic institution showcases his consistent excellence and growth as a scholar. This strong academic grounding laid the basis for his long-term contributions to machine learning, decision sciences, and interdisciplinary engineering research.
🏢 Work Experience :
Prof. Tomohiro Hayashida began his academic career in 2006 as a Research Associate at Hiroshima University, immediately after earning his Master’s degree. He was promoted to Assistant Professor in 2007, Associate Professor in 2015, and Full Professor in 2024. He has over 18 years of academic experience in teaching, research, and supervision. Beyond academic duties, he is active in government-funded research and industry collaborations, such as the ride-hailing optimization project with SmartRyde Inc. He also contributes to the Digital Manufacturing Education and Research Center at Hiroshima University. His extensive experience in both research project leadership and educational innovation showcases a balanced, impactful academic career with national and international influence.
🏅Awards and Honors
While specific award titles are not publicly listed, Prof. Hayashida’s selection as Principal Investigator for multiple highly competitive JSPS KAKEN Grants—including Young Researcher awards and Scientific Research (C) projects—reflects significant national recognition of his research excellence. His promotion to Full Professor at Hiroshima University, a top-tier Japanese institution, itself is a mark of academic distinction. He has been entrusted with strategic roles in collaborative projects, some of which have gained media coverage, such as the SmartRyde dispatch algorithm. These achievements, combined with a strong citation record and presence in high-impact journals, serve as implicit acknowledgment of his contributions to AI, optimization, and applied decision-making sciences within both academic and practical domains.
🔬 Research Focus :
Prof. Hayashida’s research centers on decision-making, machine learning, optimization, and evolutionary computation. His work addresses complex real-world problems like multi-objective scheduling, group decision analysis, and dynamic system optimization. Through JSPS-funded projects, he has developed algorithms for adaptive agents, cooperative enterprises, and evolutionary scheduling. His recent collaborations include intelligent systems for ride-hailing and dynamic dispatching. He integrates mathematical modeling with practical applications, focusing on AI-driven solutions for industries such as energy systems and transportation. His interdisciplinary approach merges operations research, computer science, and systems engineering. With over 80 publications and national/international partnerships, his research advances both academic knowledge and technological innovation, particularly in adaptive, data-driven decision systems.
📘 Integrated Optimization Method for Task Allocation and Hierarchical Reinforcement Learning in Cargo Transport Robots
🗓️ Year: 2025 | 📚 Journal: IEEJ Transactions on Electronics Information and Systems |
📄 Constrained-multiobjective Evolutionary Algorithm for Distribution System Reconfiguration under Severe Constraints
🗓️ Year: 2025 | 📚 Conference Paper |
🚚 Integrating Task Allocation and Hierarchical Reinforcement Learning for Optimized Cargo Transport Routing
🗓️ Year: 2025 | 📚 Conference Paper |
⚡ Distribution System Reconfiguration by an Evolutionary Algorithm using Constraint-Guided Dominance and Archive-Based Individual Preservation Strategy
🗓️ Year: 2024 | 📚 IEEJ Transactions on Power and Energy |
📊 Expectation and Fractile Models for Decentralised Distribution Systems under Demand Uncertainty and their Computational Methods
🗓️ Year: 2024 | 📚 International Journal of Operational Research |
🎓 WIP: Machine Learning Models for Predicting Student Performance in IoT-Enhanced Education
🗓️ Year: 2024/2025 | 📚 Conference Paper |
📈 WIP: Study on a Data-Driven Adaptive Learning Support System Design for Individualized Optimal Learning
🗓️ Year: 2024/2025 | 📚 Conference Paper |