A common-weights DEA model for centralized resource reduction and target setting

Hatami-Marbini, Adel; Tavana, Madjid; Agrell, Per Joakim; Lotfı, Farhad Hosseinzadeh; Beigi, Zahra Ghelej

doi:10.1016/j.cie.2014.10.024

Cited by 74 publications

(31 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The efficiency or inefficiency score can be obtained in terms of the distance between the evaluated DMU and a point on the technology frontier, which in turn serves as the efficient projection target. Obviously, projection target plays an important role in DEA's application (Hatami‐Marbini, Tavana, Agrell, Lotfi, & Beigi, ; Liu, Lu, Lu, & Lin, ; Lotfi, Hatami‐Marbini, Agrell, Aghayi, & Gholami, ), as it shows direction for inefficient DMU to improve performance, which is very useful for decision makers in practice. This study is to address efficient targets identification for inefficient DMUs to make inefficient DMUs efficient by allocating as few organizational or managerial costs as possible to it.…”

Section: Introductionmentioning

confidence: 99%

Target setting with minimum improving costs in data envelopment analysis: A mixed integer linear programming approach

Ang

Yang

et al. 2019

Expert Systems

View full text Add to dashboard Cite

This study addresses a problem called cost‐minimizing target setting in data envelopment analysis (DEA) methodology. The problem is how to make an inefficient decision‐making unit efficient by allocating to it as few organizational resources as possible, assuming that the marginal costs of reducing inputs or increasing outputs are known and available, which is different from previous furthest and closest DEA targets setting methods. In this study, an existed cost minimizing target setting heuristics approach based on input‐oriented model is examined to show that there exist some limitations. This study develops a simple mixed integer linear programming to determine the desired targets on the strongly efficient frontier based on non‐oriented DEA model considering the situation in the presence of known marginal costs of reducing inputs and increasing outputs simultaneously. Some experiments with the simulated datasets are conducted, and results show that the proposed model can obtain more accurate projections with lower costs compared with those from furthest and closest target setting approaches. Besides, the proposed model can be realistic and efficient in solving cost‐minimizing target setting problem.

show abstract

Section: Introductionmentioning

confidence: 99%

Target setting with minimum improving costs in data envelopment analysis: A mixed integer linear programming approach

Ang

Yang

et al. 2019

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Although DEA was initially proposed for the purpose of performance evaluation, the empirical analysis of operating status by DEA can provide some valuable insights for resource allocation (Lu and Hung, 2010). Also, it is now widely accepted that an important application of DEA-based models is resource allocation and target setting (Amirteimoori and Kordrostami, 2005;Amirteimoori and Tabar, 2010;Lotfi et al, 2013;Hatami-Marbini et al, 2015). As Beasley (2003) indicated, resource allocation is the setting of input (output) levels for DMUs when the organization has limited input resources (production possibility) and the input (output) levels should be simultaneously determined for all DMUs, whereas target setting is the setting of input (output) levels for DMUs when these can be set for each DMU individually without reference to organizational restrictions.…”

Section: Introductionmentioning

confidence: 99%

“…Towards this end, Amirteimoori and Tabar (2010) introduced goals achievement variables for the efficiency level, allocated resources, and set targets to obtain a unique allocation plan. Lotfi et al (2013) and Hatami-Marbini et al (2015) also applied a common-weights DEA approach and Goal Programming (GP) concept to allocate resources and set targets, and the post-allocation efficiency scores were maximized. Lin (2011a) proposed a DEA-based model to allocating input resources while setting output targets, and the efficiency invariance principle was reformulated in that paper.…”

Section: Introductionmentioning

confidence: 99%

Using common weights and efficiency invariance principles for resource allocation and target setting

Feng

Song

Dolgui

et al. 2017

International Journal of Production Research

View full text Add to dashboard Cite

International audienceData envelopment analysis (DEA) has proven to be a useful technique for evaluating the relative performance of comparable and homogeneous decision-making units (DMUs). In recent years, DEA-based resource allocation and target setting approaches have gained more and more attention from both practitioners and academic researchers. In this paper, we propose a new mechanism to simultaneously adopt the principles of common weights and efficiency invariance in allocating multiple resources and setting multiple targets among DMUs. To obtain the final plan, we minimize the deviation between the possible plan based on common weights and another feasible plan emphasizing efficiency invariance. If the minimum deviation equals zero, one optimal plan will be determined. In general situations, however, the proposed approach will present two plans that have a non-zero deviation. One is generated by using a common set of weights for all DMUs in such a way that the change of efficiencies are minimized, while the other is generated by strictly keeping efficiency scores unchanged yet having similar or even identical weights on input-output measures for each DMU to the utmost extent. The efficacy and usefulness of the proposed approach are demonstrated using a numerical example from previous literature and an empirical application to an urban bus company in China

show abstract

“…Nevertheless, attribute values are known, so we have to obtain attribute weights by the aforementioned approaches. However, DEA is a nonparametric programming efficiency rating technique for evaluating the relative efficiency of DMUs with multiple inputs and outputs, whose evaluation results come from input and output data [15][16][17]. Compared with other methods, attribute weights derived by DEA is relatively objective.…”

mentioning

confidence: 99%

Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development

et al. 2020

View full text Add to dashboard Cite

Multi-attribute decision making (MADM) is a cognitive process for evaluating data with different attributes in order to select the optimal alternative from a finite number of alternatives. In the real world, a lot of MADM problems involve some random and ordinal variables. Therefore, in this paper, a MADM method based on stochastic data envelopment analysis (DEA) cross-efficiency with ordinal variable is proposed. First, we develop a stochastic DEA model with ordinal variable, which can derive self-efficiency and the optimal weight of each attribute for all decision making units (DMUs). To further improve its discrimination power, cross-efficiency as a significant extension is proposed, which utilizes peer DMUs' optimal weight to evaluate the relative efficiency of each alternative. Then, based on self-efficiency and cross-efficiency of all DMUs, we construct corresponding fuzzy preference relations (FPRs) and consistent fuzzy preference relations (FPRs). In addition, we obtain the priority weight vector of all DMUs by utilizing the row wise summation technique according to the consistent FPRs. Finally, we provide a numerical example for evaluating operation performance of sustainable development of 15 listed banks in China, which illustrates the feasibility and applicability of the proposed MADM method based on stochastic DEA cross-efficiency with ordinal variable.Sustainability 2020, 12, 2375 2 of 15 two parts: classifying and ranking. Classifying can be considered as the grouping of the alternatives based on the similarities of attributes. Ranking is defined as the rank of alternatives from the optimal to the worst [8]. In recent years, some methods have been proposed to handle MADM problems, such as total sum (TS) method [9], simple additive weighting (SAW) method [10], the analytic hierarchy process (AHP) method [11], multiplicative analytic hierarchy process (MAHP) method [12], the technique for order preference by similarity to ideal solution (TOPSIS) method [13], and data envelopment analysis (DEA) method [14].In MADM problems, we need some decision making information including attribute values and attribute weights, which denote the characteristics of alternatives and relative importance of attributes, respectively [7]. Nevertheless, attribute values are known, so we have to obtain attribute weights by the aforementioned approaches. However, DEA is a nonparametric programming efficiency rating technique for evaluating the relative efficiency of DMUs with multiple inputs and outputs, whose evaluation results come from input and output data [15][16][17]. Compared with other methods, attribute weights derived by DEA is relatively objective. Therefore, DEA has been widely applied in many fields for different purposes [18][19][20], such as assessment of environmental sustainability [21,22], supplier selection [23][24][25], and evaluation of the influence of E-marketing on hotel performance [26].Due to the inherent complexity and competition of the real world, MADM problems often involve some random and ordina...

show abstract

A common-weights DEA model for centralized resource reduction and target setting

Cited by 74 publications

References 66 publications

Target setting with minimum improving costs in data envelopment analysis: A mixed integer linear programming approach

Target setting with minimum improving costs in data envelopment analysis: A mixed integer linear programming approach

Using common weights and efficiency invariance principles for resource allocation and target setting

Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development

Contact Info

Product

Resources

About