Structural Comparison of Data Envelopment Analysis and Multiple Objective Linear Programming

1998 ◽  
Vol 44 (7) ◽  
pp. 962-970 ◽  
Author(s):  
Tarja Joro ◽  
Pekka Korhonen ◽  
Jyrki Wallenius
Keyword(s):  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Multiple Objective ◽  
Multiple Objective Linear Programming ◽  
Data Envelopment ◽  
Structural Comparison

ON PREFERENCE STRUCTURE IN DATA ENVELOPMENT ANALYSIS

2005 ◽  
Vol 04 (03) ◽  
pp. 411-431 ◽  
Author(s):  
YAO CHEN
Keyword(s):  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Pareto Optimality ◽  
Direct Result ◽  
Multiple Objective ◽  
Multiple Objective Linear Programming ◽  
Data Envelopment ◽  
Preference Structure ◽  
Value Judgments ◽  
Chinese Cities

The paper studies the result of Zhu21 and establishes a relationship between the efficiency in data envelopment analysis (DEA) and the pareto optimality under multiple objective linear programming (MOLP). It is shown that the DEA/preference structure models in Zhu21 can be derived by traditional MOLP techniques. Incorporation of tradeoffs or value judgments is a direct result of using MOLP techniques. New uses of DEA are developed and described. The approach is applied to a set of Chinese cities.

scholarly journals Using Enhanced Russell Model to Solve Inverse Data Envelopment Analysis Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
G. R. Jahanshahloo ◽  
F. Hosseinzadeh Lotfi ◽  
M. Rostamy-Malkhalifeh ◽  
S. Ghobadi
Keyword(s):  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Sufficient Conditions ◽  
Necessary And Sufficient Conditions ◽  
Multiple Objective ◽  
Multiple Objective Linear Programming ◽  
Data Envelopment ◽  
Input Output ◽  
Pareto Solutions ◽  
Necessary And Sufficient

This paper studies the inverse data envelopment analysis using the nonradial enhanced Russell model. Necessary and sufficient conditions for inputs/outputs determination are introduced based on Pareto solutions of multiple-objective linear programming. In addition, an approach is investigated to identify extra input/lack output in each of input/output components (maximum/minimum reduction/increase amounts in each a of input/output components). In addition, the following question is addressed: if among a group of DMUs, it is required to increase inputs and outputs to a particular unit and assume that the DMU maintains its current efficiency level with respect to other DMUs, how much should the inputs and outputs of the DMU increase? This question is discussed as inverse data envelopment analysis problems, and a technique is suggested to answer this question. Necessary and sufficient conditions are established by employing Pareto solutions of multiple-objective linear programming as well.

scholarly journals Using goal programming method to solve DEA problems with value judgments

2014 ◽  
Vol 24 (2) ◽  
pp. 267-282 ◽  
Author(s):  
M. Izadikhah ◽  
R. Roostaee ◽  
F. Hosseinzadehlotfi
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Goal Programming ◽  
Programming Method ◽  
Programming Approach ◽  
Multiple Objective Linear Programming ◽  
Data Envelopment ◽  
Value Judgments

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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