IQRBOWとMCDMのエントロピーに基づく堅固なハイブリッド・ウェイトリング・スキーム:VIKORフレームワークにおける安定性と優位性の基準
Ali Erbey1, Üzeyir Fidan1, Cemil Gündüz1
1Department of Computer Programming, Distance Education Vocational School, Usak University, Usak 64200, Türkiye.
Entropy (Basel, Switzerland)
|August 28, 2025
まとめ
この研究では,多基準の意思決定 (MCDM) のためのハイブリッド・ウェイトング・メソッドであるIQRBOW-Eを導入し,信頼性と情報の敏感性をバランスとします. 意思決定の安定性とパフォーマンスを向上させ,特にデータ不規則性と異常値の改善に役立ちます.
科学分野:
- 運用研究
- 意思決定科学
- データサイエンス
背景:
- マルチクリテリア意思決定 (MCDM) は,特に不確実性やデータの不規則性において,信頼性の高い重み付け方法を必要とします.
- 既存の方法は信頼性がないか,重要なデータ情報を把握できないかもしれません.
- 意思決定支援システムには,正確な結果を確実にするために,適応可能な重み付けが必要です.
研究 の 目的:
- IQRBOW-E (エントロピーによる四半期間範囲ベースの目標重量) という新しいハイブリッド目標重量法を導入する.
- 調整可能なパラメータ β を使用して,統計的強度と情報の感度とのトレードオフを柔軟に制御できます.
- 不確実な環境における意思決定支援システムの適応性と信頼性を高める.
主な方法:
- IQRBOW-Eメソッドを開発し,IQRBOWの強度とパラメータβによるエントロピー情報の感度を組み合わせた.
- IQRBOW-EをVIKORの評価枠組みに統合した.
- 10つのシミュレーションシナリオで 異なる基準,代替案,異常値の比率で実験を行った.
主要な成果:
- IQRBOW-Eは,特に偏差値の汚染が増加した場合に,優れた決定安定性とパフォーマンスを示しました.
- モデルの感度を示す,異なるデータ条件に体系的に適応した最適なβ値.
- ハイブリッドアプローチは データの不規則性に対処する際に 従来の方法よりも 堅実であることが示されました
結論:
- パラメータ化されたハイブリッド・ウェイトング・モデルは,MCDMの方法論を進歩させています.
- IQRBOW-Eは,不確実な状況下での意思決定のための堅牢で一般化可能な重み付けインフラを提供します.
- この方法は,複雑なデータに直面する意思決定支援システムの適応性を高めます.
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