Finding common weights based on the DM's preference information

Jahanshahloo, G. R.; Zohrehbandian, M.; Alinezhad, Alireza; Naghneh, S Abbasian; Abbasian, H; Mavi, Reza Kiani

doi:10.1057/jors.2010.156

Cited by 23 publications

(8 citation statements)

References 22 publications

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“…The numerical results along with some qualitative assessments confirm the superiority of the proposed method when compared with the previously developed relevant techniques. Recently, finding common weights based on the DM's preference information is addressed by Jahanshahloo et al [38] in the DEA literature that measures the preferences of decision maker when generating the common weights. Accordingly, incorporating the inventory manager's preferences in a new model to derive common weights is proposed for future research in the context of MCIC.…”

Section: Discussionmentioning

confidence: 99%

A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification

Hatefi

Torabi

2015

Advances in Decision Sciences

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Organizations typically employ the ABC inventory classification technique to have an efficient control on a huge amount of inventory items. The ABC inventory classification problem is classification of a large amount of items into three groups: A, very important; B, moderately important; and C, relatively unimportant. The traditional ABC classification only accounts for one criterion, namely, the annual dollar usage of the items. But, there are other important criteria in real world which strongly affect the ABC classification. This paper proposes a novel methodology based on a common weight linear optimization model to solve the multiple criteria inventory classification problem. The proposed methodology enables the classification of inventory items via a set of common weights which is very essential in a fair classification. It has a remarkable computational saving when compared with the existing approaches and at the same time it needs no subjective information. Furthermore, it is easy enough to apply for managers. The proposed model is applied on an illustrative example and a case study taken from the literature. Both numerical results and qualitative comparisons with the existing methods reveal several merits of the proposed approach for ABC analysis.

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Section: Discussionmentioning

confidence: 99%

A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification

Hatefi

Torabi

2015

Advances in Decision Sciences

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“…The entropy of single output is defined to be 0 in Table 4; Step 5: Determining the importance degree of optimal weights as it is shown in Table 4; Step 6: Determining the common weights. By formula (18) we can get the common weights are = 1.2239, = 0.9681 and = 0.8248. Then the efficiency evaluation result is provided in Table 5.…”

Section: Illustration Examplementioning

confidence: 99%

“…Ramon et al [12,13] extended their research on the cross-efficiency evaluation into the common weights DEA method based on the idea of reducing differences between profiles of weights. Some other techniques have also been introduced into the DEA method for determining common weights, such as goral programming [14], regression analysis [15], robust optimization [16] and so on [17][18][19][20]. Applications of the common weights DEA models can be found in economy evaluation [21], technology selection [22], resource allocation [23], and so on.…”

Section: Introductionmentioning

confidence: 99%

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

2014

Entropy

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Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different multiplier weights would also be somewhat irrational in practice. The common weights DEA model is an effective method for solving these problems. In this paper, we propose a methodology combining the common weights DEA with Shannon's entropy. In our methodology, we propose a modified weight restricted DEA model for calculating non-zero optimal weights. Then these non-zero optimal weights would be aggregated to be the common weights using Shannon's entropy. Compared with the traditional models, our proposed method is more powerful in discriminating DMUs, especially when the inputs and outputs are numerous. Our proposed method also keeps in accordance with the basic DEA method considering the evaluation of the most efficient and inefficient DMUs. Numerical examples are provided to examine the validity and effectiveness of our proposed methodology.

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“…Jahanshahloo et al [18] suggested two methods to determine CSWs based on comparing ideal line and special line. Jahanshahloo et al [21] implemented Zionts-Wallenius technique to find CSWs based on the DM's preference information. Sun et al [39] introduced two models from MADA viewpoint for searching CSWs which is obtained based on applying ideal and anti-ideal units.…”

Section: Introductionmentioning

confidence: 99%

Common set of weights and efficiency improvement on the basis of separation vector in two-stage network data envelopment analysis

Kiaei

Matin

2019

Math Sci

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Common set of weights (CSWs) method is one of the popular ranking methods in DEA which can rank efficient and inefficient units. Based on an identical criterion, the method selects the most favorable weight set for all units. An important issue is that in most common DEA models, the internal structure of the production units is ignored and the units are often considered as black boxes. In this paper, in order to evaluate the units and subunits in the two-stage NDEA based on an identical criterion, it is suggested to use CSWs method on the basis of separation vector. Our research contribution in this paper includes: (1) CSWs method is formulated in two-stage NDEA as a multiple objective fractional programming (MOFP) problem. (2) A method is suggested based on separation vector to change MOFP problem into single objective linear programming (SOLP) problem in two-stage NDEA. In the theorem, it is shown that the obtained solutions from MOFP and SOLP in two-stage NDEA are identical. (3) In the framework of the new models of two-stage NDEA, a process is introduced to improve efficiency evaluation by CSWs on the basis of separation vector which is based on the radial improvement of inputs and final outputs. Finally, an enlightening application is presented.

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Finding common weights based on the DM's preference information

Cited by 23 publications

References 22 publications

A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification

A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

Common set of weights and efficiency improvement on the basis of separation vector in two-stage network data envelopment analysis

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