Robust Optimization by Fuzzy Linear Programming
| Project/Area Number |
23510169
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Osaka University |
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Principal Investigator |
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2012: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2011: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | ファジィ線形計画法 / ロバスト最適化 / 必然性測度 / グレード付きイルノウン集合 / 区間順序回帰 / 一対比較情報 / 修飾子関数 / 含意関数 / イルノウン集合 / ロバスト順序回帰 / 多目的最適化 / 必然的有効解 / k-th best法 / 必然的最適性 / 必然的有効性 / 修飾子母関数 / 可能性測度 |
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| Research Abstract |
A method for representation of the decision maker's preference on robust constraints by a necessity measure is proposed and it is shown that fuzzy linear programming problems with general necessity measures are solved without great loss of linearity. Robust optimization in two level linear programming problems and robust efficient solutions in multiple objective problems are investigated. Moreover, it is shown that approximate calculations of graded ill-known sets are rather easy. Then the linear programming problems with graded ill-known sets can be treated in a similar way to fuzzy linear programming problems. Finally, we proposed interval ordinal regression method when a part of pairwise preference information is given. We investigated the generalizations of the interval ordinal regression method.
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Report
(4 results)
Research Products
(34 results)
