Measuring performance with common weights: network DEA
نویسندگان
چکیده
منابع مشابه
Finding Common Weights in Two-Stage Network DEA
In data envelopment analysis (DEA), mul-tiplier and envelopment CCR models eval-uate the decision-making units (DMUs) under optimal conditions. Therefore, the best prices are allocated to the inputs and outputs. Thus, if a given DMU was not efficient under optimal conditions, it would not be considered efficient by any other models. In the current study, using common weights in DEA, a number of...
متن کاملRanking Efficient DMUs in Two-stage Network DEA with Common Weights method
Two stages DEA models are used in many fields of management and industry. One of the concepts that has attracted the attention of researchers in the theory of production is the concept of ranking the units with a two-stage network. A unit ranking can provide useful information to decision makers (DMUs) about optimal decision making activities. This concept defines the superiority of a unit in t...
متن کاملRanking of units on the DEA frontier with common weights
Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decisionmaking units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. This research proposes a methodology to determine one common set of weights for the performance indices of only DEA efficient DMUs. Then, these DMUs ar...
متن کاملRanking DEA Efficient Units with the Most Compromising Common Weights
One may employ Data Envelopment Analysis (DEA) to discriminate decision-making units (DMUs) into efficient and inefficient ones base upon the multiple inputs and output performance indices. In this paper we consider that there is a centralized decision maker (DM) who ‘owns’ or ‘supervises’ all the DMUs. In such intraorganizational scenario the DM has an interest in discriminating the efficient ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2019
ISSN: 0941-0643,1433-3058
DOI: 10.1007/s00521-019-04219-4