یک مطالعه در مورد توسعه مدل بهره وری مقایسه ای مبتنی بر نسبت ورودی محور
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4258||2011||5 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 3, March 2011, Pages 2473–2477
Data envelopment analysis (DEA) is a representative method to estimate efficient frontiers and derive efficiency. However, in a situation with weight restrictions on individual input–output pairs, its suitability has been questioned. Therefore, the main purpose of this paper is to develop a mathematical method, which we call the input-oriented ratio-based comparative efficiency model, DEA-R-I, to derive the input-target improvement strategy in situations with weight restrictions. Also, we prove that the efficiency score of DEA-R-I is greater than that of CCR-I, which is the first and most popular model of DEA, in input-oriented situations without weight restrictions to claim the DEA-R-I can replace the CCR model in these situations. We also show an example to illustrate the necessity of developing the new model. In a nutshell, we developed DEA-R-I to replace CCR-I in all input-oriented situations because it sets a more accurate weight restriction and yields a more achievable strategy.
نتیجه گیری انگلیسی
In this paper, we developed an input-oriented ratio-based model (DEA-R-I) for calculating efficiency scores and identifying input-target improvement strategies in situations with weight restrictions. We also show further proof of our model in order to claim that this model can replace the CCR-I model in situations without weight restrictions. A numerical example shows the difference between DEA-R-O and DEA-R-I to support our claim that the development of the DEA-R-I model is necessary for input-oriented situations with weight restrictions. This example further supports the claim that DEA-R-I can also provide easier improvement strategies than CCR-I in situations without weight restrictions. Because of its accuracy in situations with weight restrictions and its better strategy, we claim that DEA-R-I can replace CCR-I in all input-oriented situations.