A Novel Improved Data Envelopment Analysis Model Based on SBM and FDH Models

During the past decade, applying nonparametric operation research problems such as Data Envelopment Analysis(DEA) has received significant consideration among researchers. In this paper, a new DEA-based SBM-FDH model is introduced. Finally, productivity evaluation for banking systems in Malmquist Productivity Index (MPI) based on the proposed model has been compared with Slack Based Measurement (SBM) and Free Disposal Hull (FDH). The obtained results confirm the high performance of the proposed model in comparison to the other models used in this paper.

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Energy–economy–environmental (3E) performance of Chinese regions based on the data envelopment analysis model with mixed assumptions on disposability

Energy & Environment ◽

10.1177/0958305x18757057 ◽

2018 ◽

Vol 29 (5) ◽

pp. 664-684 ◽

Cited By ~ 8

Author(s):

Qingyou Yan ◽

Xu Wang ◽

Tomas Baležentis ◽

Dalia Streimikiene

Keyword(s):

Data Envelopment Analysis ◽

Central Area ◽

Malmquist Productivity Index ◽

Productivity Index ◽

Data Envelopment Analysis Model ◽

Data Envelopment ◽

Analysis Model ◽

Eastern Area ◽

Dea Models ◽

Energy Economy

This paper presents a modified environmental production technology which imposes the proper disposability on the undesirable outputs depending on the underlying technical properties. Then, aggregate and disaggregate (Russell-type) data envelopment analysis (DEA) models are proposed to evaluate the energy–economy–environment (3E) efficiency based on the modified technology (hereafter referred to as the 3E-DEA models). The non-radial Malmquist productivity index is adapted to model the changes in the 3E productivity over time. A case study of 3E efficiency analysis for the 30 Chinese administrative regions during 2011–2013 is presented. In general, Chinese regions did not perform well in terms of 3E goals as only three of them exhibited full efficiency. It was also found out that the eastern area showed the best 3E performance, whereas the central area followed suit, thus putting the western area at end of ranking. Still, some regions in the eastern area showed 3E efficiencies lower than those of some cities in the central and eastern areas. Anyway, most of the regions showed improving 3E productivity during 2011–2013.

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Evaluation of Supply Chain Efficiency Based on a Novel Network of Data Envelopment Analysis Model

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127415400362 ◽

2015 ◽

Vol 25 (14) ◽

pp. 1540036 ◽

Cited By ~ 2

Author(s):

Li Fang Fu ◽

Jun Meng ◽

Ying Liu

Keyword(s):

Supply Chain ◽

Data Envelopment Analysis ◽

Decision Makers ◽

Data Envelopment Analysis Model ◽

Data Envelopment ◽

Undesirable Outputs ◽

Analysis Model ◽

Multilayer Network ◽

Supply Chain Efficiency ◽

Proposed Model

Performance evaluation of supply chain (SC) is a vital topic in SC management and inherently complex problems with multilayered internal linkages and activities of multiple entities. Recently, various Network Data Envelopment Analysis (NDEA) models, which opened the “black box” of conventional DEA, were developed and applied to evaluate the complex SC with a multilayer network structure. However, most of them are input or output oriented models which cannot take into consideration the nonproportional changes of inputs and outputs simultaneously. This paper extends the Slack-based measure (SBM) model to a nonradial, nonoriented network model named as U-NSBM with the presence of undesirable outputs in the SC. A numerical example is presented to demonstrate the applicability of the model in quantifying the efficiency and ranking the supply chain performance. By comparing with the CCR and U-SBM models, it is shown that the proposed model has higher distinguishing ability and gives feasible solution in the presence of undesirable outputs. Meanwhile, it provides more insights for decision makers about the source of inefficiency as well as the guidance to improve the SC performance.

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A Location-Procurement-Distribution Model Combined with Data Envelopment Analysis Model under a Multi-period Environment and Gradual Budget Injection for Humanitarian Relief Supply Chains

10.21203/rs.3.rs-180144/v1 ◽

2021 ◽

Author(s):

Leyla Fazli

Keyword(s):

Data Envelopment Analysis ◽

Programming Model ◽

Mixed Integer ◽

Distribution Model ◽

Data Envelopment Analysis Model ◽

Data Envelopment ◽

Humanitarian Relief ◽

Analysis Model ◽

Multi Objective ◽

Proposed Model

Abstract Humanmade or natural catastrophes such as droughts, floods, earthquakes, storms, coups, economic and political crises, wars, and so forth impact various areas of the world annually. Furthermore, the lack of adequate preparations and proper coping against them causes nations to suffer heavy losses and casualties, which are sometimes irrecoverable. Consequently, as an essential activity in crisis management, humanitarian relief logistics has been of particular importance and has taken a good deal of notice at the international level during recent years. Aid facilities location and the storage of necessary commodities before a disaster and the proper distribution of relief commodities among demand points following a disaster are critical logistical strategies to improve performance and reduce latency when responding to a given disaster. In this regard, this study presents a stochastic multi-objective mixed-integer non-linear programming model in a two-level network that includes warehouses and affected areas. The model aims at minimizing total social costs, which include the expense of founding warehouses, the expense of procuring commodities, and deprivation cost, as well as maximizing fulfilled demands and warehouses utility. In this study, several pre-disaster periods, a limited budget for establishing warehouses and procuring relief commodities with their gradual injection into the system, the time value of money, various criteria for evaluating warehouses, the risk of disruption in warehouses and transportation networks, and heterogeneous warehouses are considered. The maximization of warehouses utility is done according to a data envelopment analysis model. Moreover, a multi-objective fuzzy programming model called the weighted max-min model is applied to solve the proposed model. Ultimately, the outcomes of the evaluation and validation of the proposed model show its appropriate and efficient performance.

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Use Shapley value for increasing power distinguish of data envelopment analysis model: An application for estimating environmental efficiency of industrial producers in Iran

Energy & Environment ◽

10.1177/0958305x19882377 ◽

2019 ◽

Vol 31 (4) ◽

pp. 656-675

Author(s):

Hashem Omrani ◽

Mohaddeseh Amini ◽

Mahdieh Babaei ◽

Khatereh Shafaat

Keyword(s):

Decision Making ◽

Data Envelopment Analysis ◽

Shapley Value ◽

Environmental Efficiency ◽

Data Envelopment Analysis Model ◽

Game Model ◽

Data Envelopment ◽

Analysis Model ◽

Proposed Model ◽

Decision Making Units

Data envelopment analysis is a linear programming model for estimating the efficiency of decision making units (DMUs). Data envelopment analysis model has two major advantages: it does not need the explicit form of production function for estimating the efficiency scores of decision making units and also, it allows decision making units to choose the weights of inputs and outputs to reach the estimated efficient frontier. In several cases, the distinguish power of data envelopment analysis model is weak and it is unable to rank decision making units, entirely. The goal of this study is to provide a better methodology to fully rank all the decision making units. First, the efficiency scores of all decision making units are generated using the cross-efficiency data envelopment analysis model and then, the cooperative game theory approach is applied to produce a fully fair ranking of decision making units. The DEA-Game model calculates the Shapley value for each coalition of decision making units and the final ranking is relied on common weights. These fair common weights are found using the Shapley value to rank decision making units, completely. To illustrate the capability of the proposed model, the industrial producers in the provinces of Iran are evaluated. First, the suitable indicators are defined and then, the actual environmental data for year 2013 is gathered. Finally, the proposed model is applied to fully rank the industrial producers in provinces of Iran from environmental perspective. The results show that the DEA-Game model can rank provinces, entirely. Based on the results, the industrial producers in big provinces such as Tehran, Fars and Yazd have undesirable performance in environmental efficiency.

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Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis

The Scientific World JOURNAL ◽

10.1155/2014/427673 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

M. Abbasi ◽

G. R. Jahanshahloo ◽

M. Rostamy-Malkhlifeh ◽

F. Hosseinzadeh Lotfi

Keyword(s):

Data Envelopment Analysis ◽

Pairwise Comparisons ◽

Data Envelopment ◽

Free Disposal Hull ◽

Free Disposal ◽

Proposed Model ◽

Using Data ◽

Almost All ◽

Do So

This paper deals with evaluating congestion in free disposal hull (FDH) models. There are several approaches in data envelopment analysis (DEA) literatures which discuss the theory and application of congestion. However, almost all of these approaches considered convex DEA technologies. So, in the case of nonconvex technologies, including FDH technology, this field is almost nil. This paper makes an attempt to fill in this void. To do so, this study provides a pairwise comparisons-based algorithm to evaluate congestion in FDH model. This algorithm identifies the sources of congestion and estimates its amounts. It is also capable of detecting the losses amounts of output due to congestion. The validity of the proposed model is demonstrated using some numerical and empirical examples.

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Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach

Sustainability ◽

10.3390/su132212547 ◽

2021 ◽

Vol 13 (22) ◽

pp. 12547

Author(s):

Myoungjae Choi ◽

Ohjin Kwon ◽

Dongkyu Won ◽

Wooseok Jang

Keyword(s):

Data Envelopment Analysis ◽

High Performance ◽

Diversity Index ◽

Data Envelopment Analysis Model ◽

Data Envelopment ◽

Analysis Model ◽

Program Unit ◽

Expected Performance ◽

The Republic ◽

Policy Direction

The Korean government has been continuously conducting diverse national R&D programs to discover new growth engines. The Republic of Korea is one of the countries with the largest investment in national R&D, but its efficiency was relatively low. In response, this study established a framework to identify the characteristics and direction of outstanding R&D programs. In this study, the performance of the R&D programs was identified in the sub-program unit. The efficiency of the national R&D program was analyzed using the data envelopment analysis model through the outputs of the national R&D programs such as papers and patents. However, patent and paper output would take time to be realized. Therefore, this study also calculated the diversity index of R&D programs to identify their potential expected performance. This study applied the suggested framework in the electric vehicle fields, which is one of the core growth engines of South Korea. A list of outstanding programs was identified from the National Institute of Science and Technology Information (NTIS) data. Additionally, this study also discovered the main technology areas and their current issues of outstanding and -new R&D programs. These results could contribute to suggesting the policy direction to conduct high-performance national R&D programs.

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A bi-level multi-objective data envelopment analysis model for estimating profit and operational efficiency of bank branches

RAIRO - Operations Research ◽

10.1051/ro/2018108 ◽

2019 ◽

Vol 53 (5) ◽

pp. 1633-1648 ◽

Cited By ~ 2

Author(s):

Hashem Omrani ◽

Setareh Mohammadi ◽

Ali Emrouznejad

Keyword(s):

Data Envelopment Analysis ◽

Data Envelopment Analysis Model ◽

Data Envelopment ◽

Analysis Model ◽

Dea Model ◽

Bank Branches ◽

Multi Objective ◽

Proposed Model ◽

Simultaneous Measuring

Data Envelopment Analysis (DEA) is a powerful method for analyzing the performance of decision making units (DMUs). Traditionally, DEA is applied for estimating the performance of a set of DMUs through measuring a single perspective of efficiency. However, in recent years, due to increasing competition in various industries, modern enterprises focus on enhancing their performance by measuring efficiencies in different aspects, separately or simultaneously. This paper proposes a bi-level multi-objective DEA (BLMO DEA) model which is able to assess the performance of DMUs in two different hierarchical dimensions, simultaneously. In the proposed model, we define two level efficiency scores for each DMU. The aim is to maximize these two efficiencies, simultaneously, for each DMU. Since the objective functions at both levels are fractional, a fuzzy fractional goal programming (FGP) methodology is used to solve the proposed BLMO DEA model. The capability of the proposed model is illustrated by a numerical example. Finally, to practically validate the proposed model, a real case study from 45 bank’s branches is applied. The results show that the proposed model can provide a more comprehensive measure for efficiency of each bank’s branch based on simultaneous measuring of two different efficiencies, profit and operational efficiencies, and by considering the level of their importance.

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Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

Mathematical Problems in Engineering ◽

10.1155/2014/121804 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Xishuang Han ◽

Xiaolong Xue ◽

Jiaoju Ge ◽

Hengqin Wu ◽

Chang Su

Keyword(s):

Decision Making ◽

Energy Consumption ◽

Data Envelopment Analysis ◽

Malmquist Productivity Index ◽

Productivity Index ◽

Productivity Change ◽

Data Envelopment ◽

Policy Makers ◽

Time Periods ◽

Decision Making Units

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

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