Project/Area Number  05452210 
Research Category 
GrantinAid for Scientific Research (B).

Research Field 
System engineering

Research Institution  KYOTO UNIVERSITY 
Principal Investigator 
IBARAKI Toshihide Kyoto University, Graduate School of Engineering, Professor, 工学研究科, 教授 (50026192)

CoInvestigator(Kenkyūbuntansha) 
福島 雅夫 奈良先端科学技術大学院大学, 情報科学研究科, 教授 (30089114)
IBARAKI Satoru Kyoto Univ.Graduate School of Eng., Assistant Prof., 工学研究科, 助手 (10252488)
NAGAMOCHI Hiroshi Kyoto Univ.Graduate School of Engineering, Assoc.Prof., 工学研究科, 助教授 (70202231)
MASUYAMA S. Toyohashi Inst.of Technology, Faculty of Engineering, Assoc.Prof., 工学部, 助教授 (60173762)
YAGIURA M. Kyoto Univ.Graduate School of Eng., Assistant Prof., 工学研究科, 助手 (10263120)

Project Fiscal Year 
1993 – 1995

Project Status 
Completed(Fiscal Year 1995)

Budget Amount *help 
¥6,800,000 (Direct Cost : ¥6,800,000)
Fiscal Year 1995 : ¥1,100,000 (Direct Cost : ¥1,100,000)
Fiscal Year 1994 : ¥1,700,000 (Direct Cost : ¥1,700,000)
Fiscal Year 1993 : ¥4,000,000 (Direct Cost : ¥4,000,000)

Keywords  graphs / networks / combinatorial optimization / metaheuristics / genetic algorithms / グラフ / ネットワーク / 組合せ最適化 / メタ・ヒューリスティックス / 遺伝アルゴリズム / メタ・ヒューリスティック / グラフ・ネットワーク / 最適化 / アルゴリズム / 近似アルゴリズム 
Research Abstract 
As evidenced by the thoretical results of NPhardness, it is well known that most of the combinatorial optimization problems are intractable. To overcome this difficulty, it is necessary to utilize the inherent structures of the problems at hand. A good example of this kind is the implicit network structures, which are often found in many problems in practice. This study pursues this direction in two respects, that is, to find efficient algorithms for some of the graph problems, and to make use of such algorithms to enhance the algorithms developed for solving various combinatorial optimization problems. As an example of the first possibility, we proposed a new efficient algorithm to compute minimum cuts in a graph, and implemented it to examine its computational performance. We furthermore used this algorithm as subalgorithms of various graph problems such as the optimum graph augmentation problem. We also considered metaheuristics, which include such algorithms as genetic algorithms, tabu search and simulated annealing, as practical tools to solve combinatorial optimization problems approximately. To compare their performance, we conducted extensive computational experiment on a testbed of the single machine scheduling problem.
