Using goal programming method to solve DEA problems with value judgments
Mohammad Izadikhah, Razieh Roostaee, Farhad Hosseinzadeh Lotfı
Abstract
Open-access reader
Mohammad Izadikhah, Razieh Roostaee, Farhad Hosseinzadeh Lotfı
Abstract
Open-access reader
Data envelopment analysis (DEA) is a linear programming approach for measuring relative efficiency of peer decision making units that have multiple inputs and outputs. DEA was developed without consideration of the decision maker?s preference structures. DEA and multiple objective linear programming are tools that can be used in management control and planning. This paper shows how a data envelopment analysis problem can be solved by transforming it into MOLP formulation. We use the goal programming method to reflect the decision making preferences in the process of assessing efficiency, such that the value judgments of the decision maker are considered. Therefore, the proposed method can find a solution that satisfies the decision maker?s goal levels. A case study is provided to illustrate how data envelopment oriented efficiency analysis can be conducted by using goal programming method.
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Data envelopment analysis (DEA) is a linear programming approach for measuring relative efficiency of peer decision making units that have multiple inputs and outputs. DEA was developed without consideration of the decision maker?s preference structures. DEA and multiple objective linear programming are tools that can be used in management control and planning. This paper shows how a data envelopment analysis problem can be solved by transforming it into MOLP formulation. We use the goal programming method to reflect the decision making preferences in the process of assessing efficiency, such that the value judgments of the decision maker are considered. Therefore, the proposed method can find a solution that satisfies the decision maker?s goal levels. A case study is provided to illustrate how data envelopment oriented efficiency analysis can be conducted by using goal programming method.
Key concepts: Data envelopment analysis, Goal programming, Linear programming, Decision maker, Computer science, Mathematical optimization, Preference, Value (mathematics)