Combination Of Data Envelopment Analysis Research Articles

Mitigating greenhouse gas emissions from sheep production system in China: An integrated approach of data envelopment analysis and life cycle assessment

Over the past decades, the global population has significantly increased, resulting in high demand for food. China, the largest sheep-producing country, has experienced 20 rapid expansions in its sheep industry, which has caused environmental consequences. This study proposes an integrated approach that combines data envelopment analysis (DEA) and life cycle assessment (LCA) to assess the ecological consequences of the sheep production system. Therefore, this work aims to evaluate the energy consumption of the existing production system and identify optimization opportunities to reduce its environmental impacts. Our results demonstrate that sheep production can decrease total energy consumption by 9.82% using optimization. Food production was reported as a critical factor in environmental impact, responsible for 69%. However, the optimized approach led to a decrease of 4,677.64 MJ in energy consumption in sheep production compared to the initial state. The findings of this study showcase the possibility of decreasing energy usage and lessening greenhouse gas (GHG) discharges in sheep farming by implementing optimization strategies.

Performance measurement of international shipping companies before and during COVID‐19: A DEA Malmquist index and window analysis approach

AbstractFor the past several decades, the container shipping industry has grown rapidly. As a result, container shipping companies (CSCs) play a vital role in the global cargo transportation market. Thus, measuring the efficiency of CSCs is a primary concern of managers and practitioners. When the COVID‐19 pandemic broke out, the industry was severely disrupted and presented with unprecedented challenges. For this reason, evaluating how CSCs performed and learning the practices they took to achieve successful business results during this period have become necessary. To serve that purpose, this article used a hybrid method that combined data envelopment analysis Malmquist index with window analysis to analyze the world's nine leading CSCs' performance over time and their efficiency relative to each other, using data collected within a 5‐year period from 2016 to 2020. Our findings revealed different characteristics and trends in efficiency among these CSCs in terms of technical efficiency change, technological change, and total productivity change. Then, managerial implications were discussed to understand the best practices of successful CSCs as well as the circumstances in which some of them underperformed during this time. In closing, the study summarized a fact‐based guideline CSCs could take as management reference should a similar crisis occur.

Multicast Routing Based on Data Envelopment Analysis and Markovian Decision Processes for Multimodal Transportation

In the context of Iraq’s evolving transportation landscape and the strategic implications of the Belt and Road Initiative, this study pioneers a comprehensive framework for optimizing multimodal transportation systems. The study implemented a decision-making framework for multimodal transportation, combining data envelopment analysis (DEA) efficiency scores and a Markov decision process (MDP) to optimize transportation strategies. The DEA scores captured decision-making unit (DMU) performance in various aspects, while the MDP rewards facilitated strategic mode selection, promoting efficiency, cost-effectiveness, and environmental considerations. Although our method incurs a total cost approximately 29% higher than MRMQoS, it delivers a nearly 26% reduction in delay compared to MCSTM. Despite MRMQoS yielding an 8.3% higher profit than our approach, our proposed scheme exhibits an 11.7% higher profit compared to MCSTM. In terms of computational time, our method achieves an average CPU time positioned between MCSTM and MRMQoS, with MCSTM showing about 1.6% better CPU time than our approach, while our method displays a 9.5% improvement in computational time compared to MRMQoS. Additionally, concerning CO2 emissions, the proposed model consistently outperforms other models across various network sizes. The percentage decrease in CO2 emissions achieved by the proposed model is 7.26% and 31.25% when compared against MRMQoS and MCSTM for a network size of 25, respectively.

Dynamic Coupling Analysis Between Scale of Network Retail Market and Green Circulation Efficiency

Abstract A sound circulation system is an important carrier for the development of the retail industry, and the promotion of green circulation efficiency (GCE) plays an important role in driving the market scale expansion of network retail (NR). This study uses the development status of China’s NR and circulation industries as basis to estimate the total carbon emissions (CE) of the circulation industry by using the system dynamics method. Moreover, this research measures China’s GCE by combining data envelopment analysis (DEA) and Malmquist index to construct a coupling analysis model to empirically investigate the dynamic coupling relationship between the NR scale and green circulation efficiency. Results indicate that China’s GCE is generally increasing, but growth rate fluctuates significantly. Additionally, the improvement of the quality and efficiency of China’s circulation industry has not yet formed a steady green path. China’s NR market scale continues to expand, but NR at this stage is still mainly concentrated in the eastern region. The efficiency of green circulation and scale of the NR market show a good coupling trend, but there is still a significant gap between the central and western regions. Accordingly, this study proposes that the construction of a green circulation system should be strengthened, and that the development of a green and efficient circulation industry should be taken as the engine of the NR scale expansion.

Corporate social responsibility and total factor productivity: the case of European mining industry

The aim of this research is to investigate the effect of corporate social responsibility (CSR) on total factor productivity (TFP) in the European mining industry, considering micro- and macroeconomic indicators of the relationship between CSR and TFP. Employing data from 40 European mining companies from content analysis, CSR Hub, and the World Bank between 2018 and 2021, this paper utilizes a combination of Data Envelopment Analysis (DEA) and panel regression techniques to test the research hypotheses. The findings suggest that the TFP of European mining firms is positively affected by CSR initiatives implemented by the companies. Also, the empirical results depict that the CSR-TFP relationship is mainly established on institutional criteria. The results also indicate that CSR-related factors, namely, transparency and reporting, training, health and safety, and resource management, are the impacting indicators. The study broadens the horizons of this line of research and can be beneficial to CEOs, managers, experts, policymakers, decision-makers, and economists in the field of mining who are willing to promote responsible and sustainable mining.

Research on Collaborative Innovation Evaluation of Intelligent Logistics Park

The collaborative innovation capability of intelligent logistics parks plays a crucial role in promoting their development, but still encounters various problems in terms of progress. On the basis of summarizing the evaluation indexes and methods of collaborative innovation capability in logistics systems, this article constructs an evaluation index system from three dimensions of intelligence, collaboration capabilities, and innovation capabilities using the combined principal component analysis and data envelopment analysis (DEA). In this empirical article, 18 intelligent logistics parks in the upper, middle, and lower reaches of the Yangtze River Economic Belt are taken as the research objects to analyze the key factors influencing collaborative innovation of practical intelligent logistics parks. Results show that, on the one hand, the collaborative innovation process of many intelligent logistics parks have redundant inputs and outputs, which leads to failure of realizing DEA effectively; on the other hand, gaps in the collaborative innovation level leads to failure of realizing the overall coordinated development. The level of economic development cannot completely determine the level of collaborative innovation of the intelligent logistics park.

A hybrid approach combining data envelopment analysis and recurrent neural network for predicting the efficiency of research institutions

The traditional data envelopment analysis (DEA) method relies solely on current and historical performance and is unable to predict future efficiency. To overcome this limitation, we propose a novel prediction model called the input–output memory network (IOMN), which extends the Recurrent Neural Network (e.g., Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) to forecast the input and output variables in future periods. We then combined the IOMN with the DEA-Malmquist index to evaluate the efficiency of Decision-Making Units (DMUs) from the past to the future. To demonstrate the effectiveness of our proposed model, we conducted a case study of 15 research institutions in the Chinese Academy of Sciences (CAS) and compared its performance with those of linear regression (LR) and support vector regression (SVR) methods. The comparative results show that the IOMN significantly outperformed both the LR and SVR methods. Finally, we fed the past and predicted future data into the DEA-Malmquist index to examine the productivity efficiency trends in research institutions over time. Our results indicate that the annual mean of total factor productivity (TFP) continued to fluctuate at approximately 1 during the observation period. In the future, the geometric mean of TFP will decline slightly by 2.3%, the geometric mean of efficiency change (EC) will decline by 5%, and the geometric mean of technical change (TC) will increase by 2.9%.

Production system efficiency optimization through application of a hybrid artificial intelligence solution

ABSTRACT Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system’s efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML-GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real-world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution.

Breaking ground in ESG assessment: Integrated DEA and MCDM framework with spherical fuzzy sets for Vietnam's wire and cable sector

In corporate sustainability reporting, the credibility of environmental, social, and governance (ESG) information is paramount, especially for socially responsible investors relying on ESG metrics. This study proposes an integrated method combining Data Envelopment Analysis (DEA), Analytic Hierarchy Process (SF-AHP), and Weighted Aggregated Sum Product Assessment (SF-WASPAS) under the Spherical fuzzy environment to assess the ESG performance of Vietnam's wire and cable companies by integrating quantitative indicators from financial statements and qualitative factors from literature reviews and expert interviews. Firstly, the DEA-Malmquist model gauges the efficiency performance of the ten wire and cable enterprises through five critical financial indicators. Secondly, using expert opinions, the SF-AHP method determines the weights of 16 selected ESG criteria, offering a qualitative dimension to the evaluation. Subsequently, the SF-WASPAS approach ranks the ten businesses based on aggregated scores derived from the criteria, forming a robust foundation for ranking. Furthermore, a comparative analysis demonstrates the practicality and reliability of our proposed framework against the Technique for Order of Preference by Similarity to Ideal Solution (SF-TOPSIS) and Fermatean fuzzy WASPAS. While the Malmquist results highlight SAM Holding Corporation (A6) with the highest average efficiency score, a holistic assessment across all three ranking methods places Vietnam Electric Cable Corporation (A1) at the forefront, closely followed by Hai Phong Electronic Mechanical JSC (A2). By harmoniously blending quantitative and qualitative perspectives and employing robust evaluation techniques, this study extends to effective resource management, targeted investor attraction, and heightened stakeholder engagement, offering substantial benefits to the wire and cable industry's ESG endeavours.

On the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithms

On the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithms

Combined Text-Mining/DEA method for measuring level of customer satisfaction from online reviews

Determining the best way to measure level of customer satisfaction (LCS) with service quality and its determinants has been a matter of concern for both practitioners and researchers. This is especially so, as LCS can be used to retain customers, sell products and services, improve the quality and value of offers and ensure more efficient and economical operating. The present study proposes a new LCS evaluation methodology that combines data envelopment analysis (DEA) with text mining to analyze online textual reviews. The proposed methodology identifies, from online reviews using a term-frequency–inverse document frequency (TF-IDF) algorithm, multiple satisfaction metrics that significantly affect customers’ service experience, quantifies them by sentiment analysis, and evaluates, by a DEA model, service providers’ LCS with respect to those metrics. To illustrate the efficacy and applicability of the proposed approach, an empirical case study applying it to the world’s top 20 airlines in 2020 was conducted. This study demonstrates how the DEA model can be effectively utilized for evaluation of LCS from online textual review data by combining it with the TF-IDF text-mining technique.

Agricultural Green Ecological Efficiency Evaluation Using BP Neural Network–DEA Model

The evaluation of agricultural green ecological efficiency can reflect the capacity of agriculture for sustainable development and reduce the endogenous pollution caused by agricultural waste in order to alleviate the weakening of agricultural ecosystems. Taking the agricultural green economy as the research object, an evaluation index system based on the theories of green economic efficiency and economic growth for agricultural green ecological efficiency was constructed, and the impact mechanisms of specific indicators on agricultural green ecological efficiency were empirically explored. In addition, based on the data envelopment analysis (DEA) model, the overall agricultural green ecological efficiency of China from 2002 to 2021 was evaluated and the efficiency characteristics were analyzed from multiple perspectives. Then, the indicators of policy, finance, communication, society and other aspects were added in order to construct a comprehensive evaluation model of agricultural green ecological efficiency using a combination of DEA and a BP neural network, and the feasibility of the model was verified. The results indicate that the agricultural green ecological efficiency increased from 0.7340 in 2002 to 0.8205 in 2021, an increase of 11.78%. Additionally, the technological efficiency of China’s agricultural green ecological system did not show a very obvious trend of divergence. The results of the BP neural network were consistent with those obtained using DEA, and the overall evolution trend of the calculated BP neural network and DEA were mutually verified and integrated. The effectiveness and accuracy of the BP neural network was verified via a comparison with DEA.

A multi-stage multi-criteria data analytics approach to rank commercial service airports

The continuous performance evaluation of commercial service airports is an important part of airport operations. Despite its significance, the existing airport ranking approaches by various aviation organizations and agencies does not provide a comprehensive methodology to rank the airports based on multiple criteria. This paper proposes a comprehensive multi-stage multi-criteria data analytics approach that combines data envelopment analysis, best-worst method, and multi-attributive border approximation area comparison method to provide a robust ranking framework. The ranking procedure is based on seven criteria, i.e. passenger throughput, number of direct routes, number of check-in counters, terminal area, number of gates, number of flights, and number of runways. The proposed method ranks the shortlisted airports from the best to the worst and evaluates the strengths and weaknesses of each of the candidate airports. A real-world example involving the ranking of the efficient major commercial service airports in China is demonstrated to validate the applicability of the proposed approach. The insights generated have implications for the benchmarking of airports.

Technological, healthcare and consumer funds efficiency: influence of COVID-19

This paper aims to analyze the efficiency of the funds in technological, healthcare, and consumer cyclical sectors based on the U.S. News & World Report rankings. We employed a Principal Component Analysis to select the indicators to explain efficiency. Then, we have used an alternative approach that combines Data Envelopment Analysis (DEA) with Multiple Criteria Decision Aiding, the Value-Based DEA, to assess the efficiency of funds for 1 year (2020), 3 years (2018–2020), and 5 years (2016–2020). The results highlight that in 2020 the number of efficient funds is much smaller than in previous periods and this can be justified by the effect of the COVID-19 pandemic crisis. The sectors with the most efficient funds are technology and healthcare. The factors that determine the efficiency of funds in the health sector and the technology sector are quite different, although they have not undergone major changes in the three periods considered. For managers, health funds are seen as low risk and hardly consider the return factors in all analyzed periods, which is often considered as benchmarks for inefficient funds. In the technology sector, Beta and Alpha are generally the indicators with the greatest weight in fund efficiency, showing that these funds beat the market in terms of returns and are less risky than the benchmark. This study seeks to complete the scarce existing literature on the subject, namely in the sectors under analysis, seeking to identify the indicators that fund managers ponder most to consider a fund as efficient. As far as we know, the joint efficiency analysis of these sectors and the impact they suffered from the COVID-19 pandemic are new in the literature.

The adjusted passenger transportation efficiency of nine airports in China with consideration of the impact of high-speed rail network development: A two-step DEA-OLS method

This paper presents a two-step method combining data envelopment analysis (DEA) and an ordinary least squares (OLS) model to analyze the passenger transportation efficiency (PTE) of nine airports in the Beijing-Tianjin-Hebei region, China, and their evolution trends from 2008 to 2019. As the first step, taking the mileage of China's high-speed rail (HSR) as an external variable, a three-stage DEA was employed to obtain the adjusted airport's PTE after eliminating the impact of HSR. As the second step, a single-logarithmic OLS model was used to analyze the influence of the urban economic structure on deviations in airports' PTE before and after the adjustment, providing a clear and straightforward basis for formulating and testing policies. The essential findings included the following: (1) the development of HSR mainly inhibited the PTE of most airports due to their competition but also promoted PTE in a few airports for short periods due to their partnership; (2) various factors of the urban economic structure had different impacts on the adjusted airport's PTE and determined the trend over time.

Estimation of technical efficiency of chemical-free farming using data envelopment analysis and machine learning: evidence from India

PurposeThis study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.Design/methodology/approachThe study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.FindingsThe results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.Research limitations/implicationsThe study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.Practical implicationsGiven the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.Social implicationsAlthough many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.Originality/valueNo prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

Production Efficiency of Raw Milk and Its Determinants: Application of Combining Data Envelopment Analysis and Stochastic Frontier Analysis

China’s raw milk production is confronted with problems such as high production costs, stringent environmental constraints, weak industrial interest linkages, etc. The extensive and inefficient operation mode seriously restricts the further development of China’s dairy industry. How to increase the production efficiency of raw milk and realize the transition from “a country with high milk production” to “a country with high-efficiency milk production” has become the key to further developing China’s dairy industry. In order to explore the upgrading path of the raw milk industry in China, this study used the DEA-Malmquist model to estimate the production efficiency of raw milk in China and analyze its spatial and temporal distribution characteristics based on raw milk production input and output data at four scales (i.e., free-range, small-scale, medium-scale, and large-scale) from 2004 to 2020. It then adopted the SFA model to explore the relationship between raw milk input and output factors and the driving factors of production efficiency. Finally, robustness was discussed according to the existing research differences. The study draws several valuable conclusions. First, the production efficiency of raw milk in China from 2004 to 2020 fluctuated upward and showed specific regular regional distribution characteristics, but the spatial–temporal differences were minor. Second, raw milk production efficiency is significantly influenced by various factors, including the proportion of concentrate to roughage consumption, medical and epidemic prevention investment, the price of raw milk, the wage level, and fixed assets. Third, the spatial–temporal distribution and driving factors of raw milk production efficiency are less robust, so efficiency analysis and improvement measures should fully consider analytical methods, scale heterogeneity, indicator systems, and temporal heterogeneity.

The hybrid DHP method for evaluation, ranking and selection of green suppliers in the supply chain

This paper presents a hybrid method called the DHP method, which is a combination of data envelopment analysis and hierarchical analysis process methods. Evaluation, ranking, and selection of green suppliers in the supply chain are important to reduce costs and increase profits, efficiency, and performance of the supply chain. The purpose of this paper is to evaluate and select the best green suppliers of wind turbine equipment using the proposed DHP approach. In fact, it deals with the process of hierarchical analysis of supplier performance and uses a cross-performance matrix instead of a weight matrix. Finally, in order to validate the proposed DHP method, a similar method is used to solve the problem of green supplier selection, and the results show the superiority of the proposed method in supplier selection. The most important advantage of the proposed approach is the simultaneous consideration of suppliers' performance and their evaluation according to the existing criteria.

Comprehensive operating efficiency measurement of 28 Chinese airports using a two-stage DEA-Tobit method

<abstract> <p>This paper presents a two-stage method combining data envelopment analysis (DEA) and a Tobit model to analyze the comprehensive operating efficiency of 28 airports in China in 2016. At the first stage, the DEA-BCC (Banker-Charnes-Cooper) model was employed to obtain the comprehensive operating efficiency of the combination of flight departure punctuality, non-cancellations, landing bridge rates from the perspective of airport infrastructure, surrounding airspace, route layouts, flight volume and weather. At the second stage, a Tobit model was used to analyze the influence of nine input variables from four aspects on obtained comprehensive operating efficiency, ultimately providing a clear and straightforward basis for formulating and testing policies. The comprehensive operating efficiency with this combination was further compared with each of the three efficiencies respectively. The important findings included the following: (1) The comprehensive operation efficiencies of most airports were greater than the individual efficiency; (2) These four types of operation efficiencies for most airports did not achieved DEA validity (100% efficiency), except for six airports (i.e., Haikou, Dalian, Jinan, Fuzhou, Nanning and Lanzhou); (3) These factors affecting each of the four types of operation efficiencies were different in that the number of terminals, duration of impact and average daily inbound and outbound flights had a negative impact on airport operational efficiency, while the average number of overnight aircraft per day and peak hour sorties had positive effects.</p> </abstract>

Combining data envelopment analysis and Random Forest for selecting optimal locations of solar PV plants

Solar photovoltaic (PV) energy has emerged as a potential alternative to carbon-based energies to meet the Paris agreement commitment. This study investigates the effect of environmental variables on the efficiency of solar PV panels. Data Envelopment Analysis (DEA) is used to estimate efficiencies of 91 solar PV panels located in Australia during the time period 2010–2020. The effects of environmental variables on the estimated efficiencies are quantified using the truncated regression model. Random forest is then used to predict efficiency of solar PV panel in every city of Australia. The results allow to determine the most suitable location and regions for solar PV energy production in Australia. This study provides an interesting and easily interpretable tool for policy decision makers.