A Practical Common Weight Scalarizing Function Approach for Technology Selection
محورهای موضوعی : Business StrategyMousa Amini 1 , Alireza Alinezhad 2
1 - Department of Industrial and Mechanical Engineering,
Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Industrial and Mechanical Engineering,
Qazvin Branch, Islamic Azad University, Qazvin, Iran
کلید واژه: DEA, Weight Restriction, Common Set of Weights, Technology Selection, Robot Selection, Scalarizing Function Approach, Discriminating Power,
چکیده مقاله :
Abstract. A practical common weight scalarizing function methodology with an improved discriminating power for technology selection is introduced. The proposed scalarizing function methodology enables the evaluation of the relative efficiency of decision-making units (DMUs) with respect to multiple outputs and a single exact input with common weights. Its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed scalarizing function framework with that obtained by the DEA classic model (CCR model) and Minimax method (Karsak & Ahiska,2005). Because the number of efficient DMUs is reduced so discriminating power of our approach is higher than previous approaches and because Spearman’s rank correlation between the ranks obtained from our approach and Minimax approach is high therefore robustness of our approach is justified.
Abstract. A practical common weight scalarizing function methodology with an improved discriminating power for technology selection is introduced. The proposed scalarizing function methodology enables the evaluation of the relative efficiency of decision-making units (DMUs) with respect to multiple outputs and a single exact input with common weights. Its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed scalarizing function framework with that obtained by the DEA classic model (CCR model) and Minimax method (Karsak & Ahiska,2005). Because the number of efficient DMUs is reduced so discriminating power of our approach is higher than previous approaches and because Spearman’s rank correlation between the ranks obtained from our approach and Minimax approach is high therefore robustness of our approach is justified.
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