A DEA model for resource allocation

Hadi-Vencheh, A.; Foroughi, Ali Asghar; Soleimani-damaneh, Majid

doi:10.1016/j.econmod.2008.01.003

Cited by 84 publications

(42 citation statements)

References 8 publications

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“…2À4 Also, as a great advantage, the performance analysis of the system can help the managers to sketch a suitable plan for allocating the budget, common revenues, rewards or shared costs to DMUs. 5,6,9,10 There are some techniques in performance analysis literature for measuring and analyzing the e±ciency of DMUs. One of the most popular ones is data envelopment analysis (DEA).…”

Section: Introductionmentioning

confidence: 99%

Efficiency Analysis of Provincial Departments of Physical Education in Iran

Soleimani-Damaneh

Soleimani-damaneh

Hamidi

2012

Int. J. Info. Tech. Dec. Mak.

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In many countries, including Iran, Provincial Departments of Physical Education try to develop the athletic sports and sports for all in their related areas (state), using the government resources. Their success rate has always been an important subject for the top sports managers of country. In this paper we use data envelopment analysis (DEA) and analytic hierarchy process (AHP) techniques for analyzing the performance of physical education organizations in Iran. Some convex and nonconvex DEA models have been used. Afterwards, we have used the Shannon's entropy for aggregating the results obtained from di®erent models and providing ā nal e±ciency score (FES) and a uni¯ed ranking. It can be seen that, in the ranking approach provided in this paper the most productive scale size (MPSS) units have the best rank (see Proposition 1). Our¯ndings reveal that the average of FESs of the states is 0.472635 and 50% of the states have an FES more than this average. Classifying the sates to¯ve e±ciency classes, \Excellent, Good, Middle, Weak and Very Weak", the percentage of the states belonging to these classes are 6.7, 30, 16.6, 36.7 and 10, respectively. Also, some correlation and di®erence studies have been carried out using the Pearson's correlation and student's t-tests. Finally, comparisons between the results of some relevant existing publications and those given in the present paper are addressed.

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Section: Introductionmentioning

confidence: 99%

Efficiency Analysis of Provincial Departments of Physical Education in Iran

Soleimani-Damaneh

Soleimani-damaneh

Hamidi

2012

Int. J. Info. Tech. Dec. Mak.

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“…Korhonen and Syrjänen [27] developed a multiple-objective linear programming model based on production technology for allocating resources. Hadi-Vencheh et al [28] proposed a strongly efficient resource allocation model based on inverse DEA which maintains DMU efficiency levels after allocation. Amirteimoori and Tabar [29] provided a DEA-based fixed-resource allocation model that considers lower and upper goals achieved by individual DMUs.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

2015

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This paper proposes a centralized data envelopment analysis (DEA) model for industrial optimization based on several different production technologies among several regions. We developed this model based on improved Kuosmanen environmental DEA technology, which avoids positive shadow price on undesirable outputs. We also designed a dual model for our centralized DEA model, and used it to analyze shadow prices on CO 2 emissions. We further employed the proposed model to determine the optimal path for controlling CO 2 emissions at the sector level for each province in China. At sectoral level, manufacturing showed the highest potential emissions reduction, and transportation was the largest accepter of emission quotas. At regional level, western and northeastern areas faced the largest adjustments in allowable emissions, while central and eastern areas required the least amount of adjustment. Because our model represents increase or decrease in emissions bidirectionally in terms of shadow price analysis, this setting makes the shadow price on CO 2 emissions lower than strong regulation (decreasing CO 2 emissions along with increasing value added) used by directional distance function (DDF).

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“…To properly solve the problem of resource allocation, many scholars have proposed approaches based on data envelopment analysis (DEA; e.g., Ding, Chen, Wu, & Wei, 2018;Du, Cook, Liang, & Zhu, 2014;Hadi-Vencheh, Foroughi, & Soleimani-damaneh, 2008;Lozano, Villa, & Brännlund, 2009;Shao, Bi, Yang, & Xia, 2018;Wu, Zhu, Chu, An, & Liang, 2016;Zhu, Wu, Li, & Xiong, 2017). DEA, which was first introduced by Charnes, Cooper, and Rhodes (1978), is an effective nonparametric method to evaluate the efficiency of a set of decision-making units (DMUs) using multiple inputs to produce multiple outputs.…”

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confidence: 99%

Allocating natural resource reduction amounts: A data envelopment analysis based‐approach considering production technology heterogeneity

et al. 2019

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Previous studies resource allocation methods based on data envelopment analysis assume that all the assessed decision‐making units share a common production technology, and all decision‐making units become efficient after the resources are allocated. However, in the real world, production technology tends to be heterogeneous among the decision‐making units because of the differences in economic development, geographic location, and market conditions. Correspondingly, when some decision‐making units are far away from the efficient frontier, they may not become efficient easily using the resources allocated to them. In this paper, we propose a data envelopment analysis‐based approach which considers production technology heterogeneity among decision‐making units when allocating resource reduction amounts to each. In our model, the decision‐making units are divided into subgroups based on their economic development level, an important indicator directly reflecting each decision‐making unit's production technology level. Each subgroup has its specific production technology, and the decision‐making units in the same subgroup have a similar technology level, which allows better identification of how the production of those decision‐making units can change when their resource inputs change. We present an empirical example using China's mainland provinces as decision‐making units to demonstrate the practicability and applicability of our proposed model.

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A DEA model for resource allocation

Cited by 84 publications

References 8 publications

Efficiency Analysis of Provincial Departments of Physical Education in Iran

Efficiency Analysis of Provincial Departments of Physical Education in Iran

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

Allocating natural resource reduction amounts: A data envelopment analysis based‐approach considering production technology heterogeneity

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