Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

Park, Jaehun; Sung, Si-Il

doi:10.3390/su8070600

Cited by 16 publications

(10 citation statements)

References 23 publications

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“…Irrespective of the advantages of the method, the following limitations of method are; if there appears to be many DMUs and many intermediate ports between each group, the applied method can create some cumbersome where too many benchmark steps may cause significant hurdles in benchmarking practices (Park and Sung 2016). Future research should address this problem to minimize the benchmarking target pathway.…”

Section: Resultsmentioning

confidence: 99%

“…Previous studies did not conduct benchmarking studies to provide environmental performance of the ports. Limited studies can be found related to benchmarking port efficiency (Hun et al, 2010;Sharma and Yu, 2009;Park and Sung 2016). In line with such motivation, this study will benchmark the environmental efficiency of container ports against best-practices in the U.S ports.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The steps of the RDEA algorithm for identifying the stratified efficient frontier are as follows (Park and Sung, 2016):…”

Section: Recursive Data Envelopment Analysis and Ksommentioning

confidence: 99%

See 2 more Smart Citations

Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports

Park

Ghani

Gebremikael

et al. 2018

International Journal of Logistics Research and Applications

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This study provides stepwise benchmarking practices of each port to enhance environmental performance using joint application of data mining technique referred as Kohonen's Self-Organizing Map (KSOM) and Recursive Data Envelopment Analysis (RDEA) to address the limitation of conventional DEA. A sample of 20 container ports in the U.S were selected, and data on input variables (number of quay crane, acres, berth and depth), output variables (number of calls, throughput and deadweight tonnage, and CO2 emissions) are used for data analysis. Among the selected samples, eight container ports are found to be environmentally inefficient. However, there appears to be a high potential to become environmentally efficient port. In conclusion, it can be inferred that, stepwise benchmarking process using two combined methodologies substantiates that, more applicable benchmarking target set of Decision Making Units (DMUs) is be projected that consider similarity of physical and operational characteristics of homogenous ports for improving environmental efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports

Park

Ghani

Gebremikael

et al. 2018

International Journal of Logistics Research and Applications

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show abstract

“…The CD-DEA, consisting of stratification procedure, attractiveness measure, and progress measure, has proven a helpful method to construct the benchmark-learning pathway for inefficient DMUs [27,28]. Through the benchmark-learning pathway, inefficient DMUs can stepwise improve their efficiencies and reach the terminal frontier [29,30]. Generally, the CD-DEA is developed under radial measure, where the slack variables are not considered.…”

Section: Methodsmentioning

confidence: 99%

A Comprehensive Evaluation of Sustainable Development Ability and Pathway for Major Cities in China

Gao

Shiue

2017

Sustainability

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Sustainability consists of economic, environmental, and societal aspects. Concomitant with China's rapid growth, the evaluation of China's ability for sustainable development (SD) has become a topic of interest. Unlike previous studies that are based on provincial regions and focus on economic and/or environmental evaluations, we have evaluated the comprehensive SD ability of 34 major cities in China using the context-dependent data envelopment analysis (CD-DEA) and proposed benchmark-learning pathways. The results indicate that the SD ability of China's major cities may be classified and ordered from low tiers to high tiers: high energy consumption and polluting industries-intensive industries-fixed asset investments and service industries-innovation, quality of life and societal services industries. As a whole, cities along the coast have a higher ability for SD than inland cities, and southern cities also rate higher than northern cities. Cities that are prioritized by government policies and funding also have higher SD abilities than other cities. We recommend that cities with lower abilities (i.e., cities in the second, third, and fourth tiers) should adopt the following points in their developmental pathways (in this order): the control of energy consumption and environmental pollution, industry upgrading and redevelopment of the city, and the development of an environment that encourages innovation and provides ample employment. This study finds that GDP is no longer an issue that restricts the SD ability of China's major cities, as compared to other factors. As China progresses towards the sustainable cities, focus should be placed on the development of a knowledge-intensive economy, balanced development, and pollution prevention.

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“…More applications of DEA can be found in Emrouznejad and Yang [9] and Liu et al [10]. From a functional perspective, DEA is most often used for DMU ranking [11,12] and benchmarking target selection [13,14], and also used for technological forecasting [15] and clustering [16].…”

Section: Introductionmentioning

confidence: 99%

A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

Lee

Choi

2019

Sustainability

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Ranking of efficient decision-making units (DMUs) using data envelopment analysis (DEA) results is very important for various purposes. We propose a new comprehensive ranking method using network analysis for efficient DMUs to improve the discriminating power of DEA. This ranking method uses a measure, namely dominance value, which is a network centrality-based indicator. Thus far, existing methods exploiting DMU’s positional features use either the superiority, which considers the efficient DMUs’ relative position on the frontier compared to other DMUs, or the influence, which captures the importance of the DMUs’ role as benchmarking targets for inefficient DMUs. However, in this research, the dominance value is the compounded measure of both core positional features of DMUs. Moreover, a network representation technique has been used to ensure the performance of the dominance value compared to the superiority and influence. To demonstrate the proposed ranking method, we present two examples, research and development (R&D) efficiency of small and medium-sized enterprises (SMEs) and technical efficiency of plug-in hybrid electric vehicles (HEVs). Through these two examples, we can see how the known weaknesses and the unobserved points in the existing method differ in this new method. Hence, it is expected that the proposed method provides another new meaningful ranking result that can show different implications.

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Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

Cited by 16 publications

References 23 publications

Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports

Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a U.S. container ports

A Comprehensive Evaluation of Sustainable Development Ability and Pathway for Major Cities in China

A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

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