We demonstrate the proposed method using data that increase energy efficiency and heating gas emissions in the European Union (EU) countries.
📌 3 Key Takeaways
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Objective & Hypothesis:Abstract Data envelopment analysis (DEA) is a linear programming based tech- nique for measuring the relative performance of organisational units where the presence of multiple inputs and outputs makes comparisons difficult.
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Key Findings & Data:We used, Morita and Avkiran propose after it has been de- veloped an input-output selection method that uses fractional factorial design, which is a statistical approach to find an optimal combination.
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Conclusion & Impact:We demonstrate the proposed method using data that increase energy efficiency and heating gas emissions in the European Union (EU) countries.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
Abstract Data envelopment analysis (DEA) is a linear programming based tech- nique for measuring the relative performance of organisational units where the presence of multiple inputs and outputs makes comparisons difficult. We used, Morita and Avkiran propose after it has been de- veloped an input-output selection method that uses fractional factorial design, which is a statistical approach to find an optimal combination. Energy efficiency and greenhouse gas emissions are closely linked in the last two decades. We demonstrate the proposed method using data that increase energy efficiency and heating gas emissions in the European Union (EU) countries.
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Cite This Article
Jarjies, O., Bayrak, H., Durukan, K. (2022). Using of fractional factorial design $ (r ^{k-p} )$ in data envelopment analysis to selection of outputs and inputs. Hacettepe Journal of Mathematics and Statistics, Cilt: 44 - Sayı: 3, 679–687. https://ajindex.com/articles/530267
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