Research Support Office Research Advancement Division. Tokyo University of Agriculture and Technology
| TEL | +81-42-367-5944 |
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| FAX | +81-42-367-5946 |
This program is supported by MEXT’s scientific technology human resource development fee grant, "Program to Disseminate Tenure Tracking System".
Home > Tenured Faculties > Miyashiro Ryuhei
Miyashiro Ryuhei
| Affiliation | Institute of Engineering |
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| Division | Division of Advanced Information Technology and Computer Science |
| Research field | Operations Research |
| Keyword(S) | Combinatorial Optimization, Mathematical Programming, Algorithm |
| Url | http://web.tuat.ac.jp/~miya/ |
| Research experience | May 2004 - May 2004: Research Fellow, Graduate School of Information Science and Technology, The University of Tokyo June 2004 - September 2009: Assistant Professor, Institute of Symbiotic Science and Technology, Tokyo University of Agriculture and Technology October 2009 - March 2010: Associate Professor, Institute of Symbiotic Science and Technology, Tokyo University of Agriculture and Technology April 2010 - September 2014: Associate Professor, Institute of Engineering, Tokyo University of Agriculture and Technology September 2014 - Present: Associate Professor(Tenured), Institute of Engineering, Tokyo University of Agriculture and Technology |
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| Educational background | The University of Tokyo, Faculty of Engineering, Graduated, 1999 The University of Tokyo, Ph. D (Information Science and Technology), March 2004 |
| Awards | * The latest information is shown at the member's website. |
| Selected papers and publications | * The latest information is shown at the member's website. R. Miyashiro, T. Matsui: Semidefinite programming based approaches to the break minimization problem. Computers and Operations Research, 33 (2006), 1975-1982. R. Miyashiro, Y. Fukagawa: Optimization of alignment in semiconductor lithography equipment. Precision Engineering, 33 (2009), 327-332. R. Miyashiro, S. Imahori, T. Matsui: An approximation algorithm for the traveling tournament problem. Annals of Operations Research, 194 (2012), 317-324. R. Miyashiro, Y. Takano: Subset selection by Mallows' Cp: a mixed integer programming approach, Expert Systems with Applications, 42 (2015), 325-331. R. Miyashiro, Y. Takano: Mixed integer second-order cone programming formulations for variable selection, European Journal of Operational Research, 247 (2015), 721-731. |
My research interest is in the field of operations research, particularly in combinational optimization and mathematical programming. We encounter a wide variety of optimization problems in daily life, especially in technology-related problems. The aim of optimization is to find the best solution under given constraints; various approaches are available for solving optimization problems. In the field of combinational optimization and mathematical programming, one first describes the optimization problem to solve in a form of mathematical equations, and then tries to find the true optimal solution for the modeled equations. Pursuit of true optimal solutions is a characteristic and difficulty of these research fields. Methods of combinatorial optimization and mathematical programming are multidisciplinary; these procedures can tackle versatile problems. Needs for these research fields will become increasingly important.
"The most attractive aspect of the TUAT tenure-track program is that each researcher can establish a laboratory independent of others. In addition, the program gives tenure-track researchers reduction in business other than his/her researches, such as lectures. Although there might be another method of tenure-track programs, the TUAT one provides one of desirable ways for researchers who has a young academic-career.
Considering ever growing masses of data in this information age, needs for strict and true optimization will become increasingly important. To find truly optimized solutions to large-scale and tough optimization problems, we have no alternative at the moment to combinational optimization and mathematical programming. These considerations encourage us to develop modeling used for mathematical programming to solve large scale problems rapidly. Furthermore, we wishes to establish a systematic framework of mathematical modeling that connects optimization problems of any type to proper patterns of modeling.
