A New Method for Allocating Fixed Costs with Undesirable Data: Data Envelopment Analysis Approach
محورهای موضوعی : Financial MathematicsMohhamad Reza Mozafari 1 , Marzieh Ghasemi 2 , Farhad Hosseinzadeh Lotfi 3 , Mohsen Rostamy-Malkhalifeh 4 , Mohammad Hasan Behzadi 5
1 - Department of Mathematics, Shiraz University Branch, Islamic Azad University, Shiraz, Iran
2 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Mathematics, Science and Research Branch, Islamic Azad University,Tehran, Iran
4 - Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
5 - Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Fair allocation, Data Envelopment Analysis(DEA), undesirable data, Fixed costs,
چکیده مقاله :
Allocating fixed costs with undesirable data has recently been one of the most important issues for managers to discuss. Lack of attention to undesirable data may lead to incorrect cost allocation. Considering and determining undesirable inputs and outputs, data envelopment analysis (DEA) technique can be significantly helpful in determining the cost allocation strategy. In-puts and outputs are divided into two desirable and undesirable groups. Obviously, desirable inputs and undesirable outputs must be reduced and undesirable inputs and desirable outputs must be increased to improve performance. This manuscript presents two strategies for allocating fixed costs with undesirable data. In the first strategy, each decision making unit (DMU) first determines the minimum and maximum shares that it can receive from the fixed resources while the efficiency of that DMU and other DMUs re-mains the same after receiving the fixed resources. Finally, the decision maker chooses the fixed cost for each DMU between the minimum and maxi-mum cost values proposed. In the second strategy, the allocation of fixed costs is done using the CCR multiplicative model with undesirable data. The effectiveness of both methods is examined by an applied study on the commercial banks.
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