Management System Engineering Laboratory

H. Katagiri

Hideki Katagiri

Office: 23-409 Ext.: 3716 Laboratory 23-408 Ext: 3715


03/2000, Ph. D., Osaka University

03/1997, M. E., Osaka University

Research Field

Operations Research, Systems Optimization, Data Analysis

Research Subjects

  • 1. Soft computing approaches to optimization problems under uncertainty.
  • 2. Production and logistics systems optimization.
  • 3. Machine learning-based anomaly detection.


Owing to the progress of globalization caused by the rapid development of ICT, many difficult problems that need to be solved in large-scale complex systems have emerged. These problems are difficult because of the uncertainty of the environment and the diversity of individuals and organizations. To provide new technologies and valuable concepts for decision making methods, we conduct studies on systems optimization under uncertainty, data analysis, or business analytics based on soft computing, artificial intelligence, and machine learning. In cooperation with companies and other research institutes, we have performed several collaborative research projects to solve problems in manufacturing and in service industries such as tourism, medicine, and health.

  • 1) H. Katagiri, Q. Guo, H. Wu, H. Hamori, K. Kato, “A route optimization problem in electrical PCB inspections: pickup and delivery TSP-based formulation”, In: G.-C. Yang, S.-I. Ao, X. Huang, O. Castillo (eds.) Transactions on Engineering Technologies, pp. 193-205, Springer (2016).
  • 2) H. Katagiri, T. Uno, K. Kato, H. Tsuda and H. Tsubaki, “Random fuzzy bilevel linear programming through possibility-based value at risk model”, International Journal of Machine Learning & Cybernetics, vol. 5, pp. 211–224 (2014).
  • 3) H. Katagiri, K. Kato and T. Hasuike, “A random fuzzy minimum spanning tree problem through a possibility-based value at risk model”, Expert Systems with Applications, vol. 39, pp. 10639–10646 (2012).
  • 4) H. Katagiri, I. Nishizaki, T. Hayashida and T. Kadoma, “Multiobjective evolutionary optimization of training and topology of recurrent neural networks for time-series prediction”, The Computer Journal, vol.55, pp.325–336 (2012).
  • 5) A. Azaron, C. Perkgoz, H. Katagiri, K. Kato and M. Sakawa, “Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm”, Computers & Operations Research, vol. 36, pp. 1562–1571 (2009).
Affiliated Academic Organizations

Institute for Operations Research and the Management Sciences, Japan Association for Management Systems, Association Society for Fuzzy Theory Intelligent Informatics, INFORMS, IEEE

Current members
◯ Professors: 1 ◯ Undergraduates: 10