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...
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In the Data Envelopment Analysis (DEA) the efficiency of the units can be obtained by identifying the degree of the importance of the criteria (inputs-outputs).In DEA basic models, challenges are zero and unequal weights of the criteria when decision- making units are evaluated. One of the strategies applied to deal with these problems is using common weights of the each input...
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Current weightless classifiers require historical data to model a system and make prediction about a system successfully. Historical data either is not always available or does not take a recent system modification into consideration. For this reason an adaptive filter is designed, which when employed with a weightless classifier enables system model, difficult to characterise system model, and...
متن کاملEfficient computation of 2-medians in a tree network with positive/negative weights
We consider a variant of the classical two median facility location problem on a tree in which vertices are allowed to have positive or negative weights. This problem was proposed by Burkard et al. in 2000 (R.E. Burkard, E. Çela, H. Dollani, 2-medians in trees with pos/neg-weights, Discrete Appl. Math. 105 (2000) 51–71). who looked at two objectives, finding the total minimum weighted distance ...
متن کاملPredictive gains from forecast combinations using time varying model weights ∗
Several frequentist and Bayesian model averaging schemes, including a new one that simultaneously allows for parameter uncertainty, model uncertainty and time varying model weights, are compared in terms of forecast accuracy over a set of simulation experiments. Artificial data are generated, characterized by low predictability, structural instability, and fat tails, which is typical for many f...
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ژورنال
عنوان ژورنال: Frontiers of Engineering Management
سال: 2020
ISSN: 2095-7513,2096-0255
DOI: 10.1007/s42524-020-0109-1