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By William W. Cooper
Creation to info Envelopment research and Its makes use of: With DEA-Solver software program and References has been conscientiously designed by means of the authors to supply a scientific creation to DEA and its makes use of as a multifaceted instrument for comparing difficulties in numerous contexts. The authors were fascinated about DEA's improvement from the start. William Cooper (with Abraham Charnes and Edwardo Rhodes) is a founding father of DEA. Lawrence Seiford and Kaoru Tone were actively concerned as researchers and practitioners from its earliest beginnings. All were deeply taken with makes use of of DEA in functional functions in addition to within the improvement of its easy thought and methodologies. the result's a textbook grounded in authority, adventure and substance.
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Extra info for Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References
Sample text
8. After the computation is over, click the "Exit" button. 9. Open the sheet "Weight" which contains optimal weights obtained for each hospital. 7. 7. Optimal Weights for Hospitals A and B No. 00611. These weights give the best ratio score 1 (100%) to each hospital. 6. 0081 are applied for this fixed weight evaluation and these also made Hospital A efficient. 2 The comptroller and treasurer of an industrial enterprise discuss whether the company's sales should be treated as an input or an output.
Only DMU B is efficient and is in the reference set of all of the other DMUs. 1 portrays the situation geometrically. The efficient frontier represented by the solid line passes through B and no other point. 2 show what is needed to bring each DMU onto the efficient frontier. 2. 5000 0,6250 B B B B B B B B to A's input will bring A onto the efficient frontier by reducing its input 50% while leaving its output at its present value. 6667 x 3 = 2 will position C on the frontier. And so on. 6n Efficient Frontier 40) CO y m H «x-^ 5- 3 - X *o '^ 00 2- •c •G V 1 0- C , ,— 2 1 1 1 1 3 4 5 6 1— 1 1 Employee Figure 2 .
3 THE CCR MODEL Given the data, we measure the efficiency of each DMU once and hence need n optimizations, one for each DMUj to be evaluated. , n. ,5) as variables. (F^) max e = ^^y^o+u,y,. -. , ViXio + V2X20 H '— 1- VmXmo '- < 1 0 = 1 , . . 5) u i , i i 2 , . . , ' U s > 0. 6) The constraints mean that the ratio of "virtual output" vs. "virtual input" should not exceed 1 for every DMU. The objective is to obtain weights {vi) and {ur) that maximize the ratio of DMUo, the DMU being evaluated. By virtue of the constraints, the optimal objective value ^* is at most 1.