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This study aims to present the meaningful implications of introducing a tramway as a new mode of transportation in the Daejeon Metropolitan Area, a major metropolitan area in South Korea. An efficiency comparison by data envelopment analysis (DEA) was carried out, using variables selected from the 2021 Public Transportation Investigation by the Korea Transportation Safety Authority (KTSA) (2021), and the ‘Guidelines for selecting new transportation means’ announced by the Ministry of Land, Infrastructure, and Transport in 2021. As a result, the efficiency of public transportation in major metropolitan areas outside of the capital region was higher than in the capital metropolitan area. In particular, the Daejeon Metropolitan Area ranked high in efficiency compared to other major metropolitan areas with similar conditions. In the V-super efficiency results of the efficiency model based on the input of operational costs for each new mode of transportation, considering variable returns to scale (VRS), the bus rapid transit (BRT) ranked first, the tramway second, and the bimodal tram third. Regarding construction cost input, the tramway ranked first, the bimodal tram second, and BRT third.

In interval data envelopment analysis (DEA), the production possibility set is variable, which causes traditional resource allocation optimization methods to yield results with limited reachability. This study aims to improve existing resource allocation optimization models so that they can produce meaningful results when handling interval data. Addressing this topic can enhance the applicability of existing models and improve decision-making accuracy. We grouped decision-making units (DMUs) based on heterogeneity to form production possibility sets. We then considered the characteristics of the worst and best production possibility sets in the interval DEA to establish multiple benchmark fronts. A staged optimization procedure is proposed; the procedure provides a continuous optimization solution, offering a basis for decision-makers to formulate strategies. To illustrate this, we provide a numerical example analysis and a case study on forest carbon sequestration. Finally, by applying our method to China’s forest carbon sink data, we show that it better meets the practical needs in China. The practical implication of this procedure is that it provides a basis for decision makers to formulate strategies based on interval data. The theoretical implication is that it extends the application of DEA models to interval data.

Micro and large-sized enterprises are important elements to enhance the economic growth of any country, and even more so for developing countries such as Mexico. These enterprises highly contribute to job generation, competitiveness, and gross domestic product, factors that are important for the developing of a nation. The aim of this paper is to study the impact of human capital investments in the efficiency of the 21 economic activity subsectors for micro and large-sized enterprises in the Mexican manufacturing industry between 2009–2021. The database come from Mexico Annual Manufacturing Industry Survey. Four Data Envelopment Analysis models are developed to study the relationship between annual average working days, annual average wages, and annual average investment in training with average sales per year. Data indicate that, most of the micro-sized enterprises of the Mexican manufacturing sector do not invest in human capital training, contrary to their large-sized enterprises. The results show that investing in human capital training increase sales and wages in micro-sized enterprises of the Mexican manufacturing industry, but it is not evident in large-size enterprises of the Mexican manufacturing industry. The calculation of the economic activity subsectors efficiencies using the developed Data Envelopment Analysis models indicate that all the economic activity subsectors with scale efficiency equal to one optimally invest, and the average amount of investments in human capital training needed to increase the global and pure technical efficiencies of the others are calculated with the developed Data Envelopment Analysis models. In the three main economic activity subsectors of the Mexican manufacturing industry, a significant increase—in 83.33% of cases—in wages and salaries is seen in both micro and large-sized enterprises. Particularly, the results indicate that the Chemical industry economic activity subsectors show the highest efficiency in both micro and large-sized enterprises when the human capital training variable is present. This paper demonstrates the importance of investing in human capital to enhance the efficiency of micro and large-sized enterprises.

Green hydrogen can be generated through electrolysis using electricity from renewable sources, such as wind and solar, to split water into hydrogen. This study evaluates the green hydrogen supply chain (GHSC) upstream process using the two-phase integrated multi-criteria decision analysis (MCDA) framework. In the first phase, the data envelopment analysis (DEA) technique is applied to measure the relative efficiency of provincial alternatives with multiple criteria. The input criteria include provincial area, population density, gross domestic product value, and land cost data. In contrast, the sustainability-based governmental criteria concerning people, prosperity, planet, peace, and partnership indices are used as output criteria. Then, the technique for order of preference by similarity to ideal Solution (TOPSIS) is further applied to evaluate regional districts for Ubonratchathani province, one of the twelve relatively efficient provinces, to obtain the ranking list of potential renewable energy sites in the GHSC. Criteria related to geographic and climate data relevant to the efficiency of solar and wind are, thus, collected and analyzed. Our results show that the top three district areas are Kut Khaopun District, Pho Sai District, and Na Tan District, respectively. Finally, the obtained results are verified to evaluate the robustness of the assessment. Our results offer a strategic and practical analysis for policymakers involved in the energy site appraisal process.

Amid rising global demand for renewable energy, geothermal power emerges as a vital, low-carbon solution to enhance energy security and sustainability. Taiwan, strategically located on the seismically active Pacific Ring of Fire, possesses an untapped geothermal potential that is underutilized due to complex site selection challenges. This study specifically addresses the need for a more precise and psychologically attuned site selection process, aiming to optimize the development of geothermal resources in regions with complex geological settings. Utilizing the Modified Bounded Rationality Data Envelopment Analysis (MB-DEA) model, this research integrates traditional DEA with bounded rationality to factor in the risk preferences of decision-makers, offering a novel approach that enhances accuracy in evaluating geothermal sites. This study addresses the critical challenge of accurately selecting geothermal energy sites in geologically complex regions like Taiwan, where traditional methods fall short, aiming to significantly boost the efficiency and effectiveness of geothermal energy exploitation as part of Taiwan’s transition to renewable energy sources. Applied to 30 potential sites across Taiwan, our model provides a detailed assessment based on technical, economic, and psychological criteria, revealing variations in site suitability influenced by stakeholder risk attitudes. Key locations such as Datun Mountain, Maoxing, and Taolin consistently rank highly, confirming their robust potential irrespective of risk preferences. At the same time, other sites show marked sensitivity to shifts in decision-making attitudes. This work significantly advances the methodology of renewable energy site selection by demonstrating the utility of incorporating psychological factors into analytical models, which not only refines decision-making processes but also aligns with Taiwan’s strategic energy planning goals. This study also underscores the importance of accurate geographical data in complex terrains, suggesting further refinement and dynamic integration of bounded rationality for future research.

This study focuses on evaluating the technical and scale efficiencies of smallholder pineapple farmers in Ghana’s Central Region. We surveyed 320 participants selected using random sampling and applied an input-oriented Data Envelopment Analysis (DEA) method to gauge their technical, pure, and scale efficiencies. Our findings indicate that the mean technical efficiency among these farmers is 0.505, with individual scores ranging from 0.079 to 1.000. Notably, 90.82% of the farmers are operating below maximum efficiency levels, suggesting a potential input reduction of up to 49.5% while maintaining current production levels. Relaxing the assumption of constant returns under Variable Returns to Scale (VRS) conditions reveals a notable improvement in technical efficiency, with 10.82% more farmers achieving optimal efficiency levels. Furthermore, our analysis highlights scale inefficiencies, with 67.26% of farmers operating below optimal scale levels. By increasing production by 22.8%, these scale-inefficient farmers could enhance their efficiency and productivity within existing technological frameworks. These findings underscore the importance of collaborative efforts among policymakers, practitioners, and stakeholders within the agricultural value chain to implement interventions such as improving access to technology and innovation for smallholder farmers and making necessary investments in farmer education and training programs to enhance both technical and scale efficiencies in Ghana’s pineapple sector. Such initiatives can drive sustainable growth, improve farmers’ livelihoods, and bolster the sector’s overall competitiveness.

Energy innovation plays an important role in the transition to a zero-carbon economy. Governments in IEA member countries are investing in the R&D, demonstration, and deployment of new energy technologies as part of their energy and climate policies. However, government subsidies for energy innovation are not always efficient in achieving climate policy goals. This paper proposes a two-stage Data Envelopment Analysis model with shared inputs to determine the optimal allocation of public funds for the energy innovation process. The innovation process is divided into two stages: the R&D stage and the commercialization stage. The inputs to the model (budget expenditures for energy innovations) are distributed between the first and second stages. As intermediate products, we use the number of patents in clean energy and hydrocarbon energy. The outputs of the model are the changes in carbon intensity and energy efficiency. This model can be used to assess the effectiveness of government spending on energy innovation. The results show that some IEA member countries should allocate a large part of the fossil fuel technology budget (more than 70%) to the research and development phase. The proposed model can support decision making at the international level to increase the effectiveness of public policies in achieving decarbonization and energy efficiency goals.

During the 2008 financial crisis, central banks (such as the Fed) adopted a quantitative easing (QE) policy to stimulate their countries’ economies and overcome severe economic and financial recessions. However, apart from stimulating the economy by issuing a substantial amount of currency to purchase long-term bonds and suppress interest rates, QE policy also contributed to a boom in the real estate and construction sectors. Therefore, this study employs data envelopment analysis to measure the business performance (BP) of construction companies, and explore the impact of QE policy on the BP of construction companies, between 2004 and 2015, using hierarchical regression. We also examine the moderating role of the debt ratio on the relationship. Focused on publicly listed construction companies in Taiwan, this research reveals three encouraging findings. Firstly, QE policy indeed enhanced the BP of Taiwanese construction companies. Secondly, performance improvements in construction companies due to QE policy show a time-diminishing trend, suggesting the importance of seizing the initial policy benefits of QE implementation. Lastly, construction companies with appropriate financial leverage may exhibit better BP. These findings can provide valuable insights for relevant government entities and decision-makers in the industry for policy and investment decisions.

Since the 1990s, market liberalization of the electricity industry has advanced all around the world. To survive in the drastically changing business environment, incumbent electric utility companies have conducted operational reforms, including Mergers and Acquisitions (M&As), to enhance and/or complement existing business capabilities. The purpose of this study was to measure the operational efficiencies of 31 of the world’s largest electric utility companies using data from 2010 to 2020 and examine regional differences in and the impacts of M&As on the efficiencies. For this purpose, we applied a new type of Data Envelopment Analysis (DEA) and Tobit model regression. We provide findings from the empirical analyses and discuss the business implications of M&As for electric utility companies. The operational efficiency measures were different among regions, but did not show statistically significant changes over the study period from 2010 to 2020. Furthermore, the results of regression analyses indicate that the increasing number of M&A buying transactions and M&A total transactions has a negative marginal impact on the operational efficiency or leads to a lower operational efficiency for utility companies. Since electricity utility companies have not received gains in operational efficiency from increasing the number of M&A transactions, they need to be more cautious about whether M&A transactions can provide value to the operation and technology management.

The current paper presents the results of a study that analyzed and evaluated the efficiency of the largest container ports in the wider area of the Mediterranean Basin. The research question that this paper seeks to respond to is how the resources (inputs) of a container port reflect its level of activity and efficiency. In particular, what is the relationship between ports’ infrastructures, equipment and their productivity and the ports’ ability to attract economic activities as well as the extent of their effect on a port’s efficiency? The methodology uses the data envelopment analysis (DEA) output-oriented model following a cross-sectional approach. The research conducts two modeling approaches, the CCR and the BCC model. The analysis goes deeper and compares port efficiency estimates in relation to medium-sized and large ports’ classification and their total market share. The main findings indicated an average efficiency of 0.88 and 0.89 assuming constant and variable returns of scale, respectively, implying that the ports can increase their output levels up to approximately 1.2 times without any change in their inputs.

This comparative study analyzed the impact of loan portfolio composition on the efficiency of different types of banks in India—public sector, old private, and new private banks—in the period between 2013 and 2022. Efficiency was evaluated using data envelopment analysis (DEA). The study considered four loan variables—term lending, working capital, priority sector lending, and secured lending in proportion to the overall loans—as independent factors against the efficiency score as the dependent variable, using a random-effects generalized least squares (GLS) regression framework. The results indicate that there were no significant effects on the efficiency of old private banks, except for working capital, which had a marginally negative impact on bank efficiency. Working capital, priority sector lending, and term lending have been found to significantly impact the efficiency of new private banks. Only term and working capital loans significantly affected the efficiency of public sector banks.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

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.

Data envelopment analysis (DEA) has been proposed as a means of assessing alternative management options when there are multiple criteria with multiple indicators each. While the method has been widely applied, the implications of how the method is applied on the resultant management alternative ranking have not been previously considered. We consider the impact on option ranking of ignoring an implicit hierarchical structure when there are different numbers of indicators associated with potential higher-order objectives. We also consider the implications of the use of radial or slacks-based approaches on option ranking with and without a hierarchical structure. We use an artificial data set as well as data from a previous study to assess the implications of the approach adopted, with the aim to provide guidance for future applications of DEA for multi-criteria decision making. We find substantial benefits in applying a hierarchical approach in the evaluation of the management alternatives. We also find that slacks-based approaches are better able to differentiate between management alternatives given multiple objectives and indicators.

This paper introduces an improved slack-based game cross-efficiency measurement model that enhances the existing cross-efficiency framework and integrates it with the Data Envelopment Analysis (DEA) game cross-efficiency. The model ensures the fairness of its results through the implementation of a more stringent selection of frontier face weights. It accounts for the competitive relationships among Decision Making Units (DMUs), achieving a Nash equilibrium solution through continuous iterations. Furthermore, the model accounts for undesirable outputs and various strategic orientations, enhancing its applicability. The model’s effectiveness is validated through comparative analyses of diverse case studies. Additionally, the model’s practical utility is demonstrated through the analysis of industrial data from various Chinese provinces between 2010 and 2019. Analysis results show that the proposed model measures production efficiency with greater precision and comparability than alternative models.

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector.

The COVID-19 pandemic has profoundly impacted global economies, underscoring the urgency of deriving lessons to enhance future crisis preparedness. This study explores the effects of monetary recovery policies on supply chain dynamics across key global cities during the pandemic’s initial phase, emphasising policy interactions, industry engagement, and economic resilience. Utilising principal component analysis (PCA), data envelopment analysis (DEA), and tobit regression, we present a pioneering method to unravel the complex relationship between economic policies and urban supply chains. PCA simplifies data complexity and reveals complex policy-resilience relationships, while DEA facilitates a comparative efficiency analysis. Our findings underscore the critical importance of supply chain resilience in fostering early economic recovery, indicating that cities implementing diverse, sector-specific policies achieved more notable improvements in gross domestic product (GDP). This research not only advances methodological approaches for policy evaluation but also provides valuable insights for optimising urban economic recovery strategies amidst global challenges.

Shared resources are common among supply chain partners and also occur in multiple linked stages of an internal network. The sharing of these resources impacts the organization’s profits. This study is focused on the potential benefits of resource sharing on a three-stage network system and on the profit improvement allocation. Previous treatments concentrate on defining optimal proportions to allocate resources and disregard the impacts of allocations to promote cooperation and are limited to static evaluations. Data Envelopment Analysis performs the decision-making units (DMUs) efficiency measurement. Methodological advances have resulted in models that analyze their internal structure and temporal impacts on efficiency. We propose an integrated cooperative game and dynamic network DEA that considers known quantities of resources used in each stage and the time effects to optimize the system’s profit. Each DMU stage is a player, and we investigate performance before and after resource sharing. Using Shapley value and Nucleolus, it is possible to allocate the benefits obtained based on the marginal contributions of each stage, providing incentives to motivate and maintain cooperation. A numerical example is used to illustrate the method. The results confirm the identification of inefficient DMUs and that sharing resources allows for profit increase for all of them.

The Global Malmquist Productivity Index (GMPI) stands as an evolution of the Malmquist Productivity Index (MPI), emphasizing global technology to incorporate all-time versions of Decision-Making Units (DMUs). This paper introduces a novel approach, integrating the Nash Bargaining Game model with GMPI to establish a Cross-Productivity Index. Our primary objective is to develop a comprehensive framework utilizing the Nash Bargaining Game model to derive equitable common weights for different time versions of DMUs. These weights serve as a fundamental component for cross-evaluation based on GMPI, facilitating a holistic assessment of DMU performance over varying time periods. The proposed index is designed with essential properties: feasibility, non-arbitrariness concerning the base time period, technological consistency across periods, and weight uniformity for GMPI calculations between two-time versions of a unit. This research amalgamates cross-evaluation and global technology while employing geometric averages to derive a conclusive cross-productivity index. The core motivation behind this methodology is to establish a reliable and fair means of evaluating DMU performance, integrating insights from Nash Bargaining Game principles and GMPI. This paper elucidates the rationale behind merging the Nash Bargaining Game model with GMPI and outlines the objectives to provide a comprehensive Cross-Productivity Index, aiming to enhance the robustness and reliability of productivity assessments across varied time frames.

Policy guidance is a key driving force for improving the business performance of marine resource enterprises. This study establishes a DEA-fsQCA model, selects 42 listed marine resource enterprises as samples, analyzes the business performance improvement paths of marine resource enterprises, and proposes relevant policy recommendations for the government to guide marine resource enterprises to improve their business performance. The result shows that there are three different path models for the high business performance of marine resource enterprises based on their scale and property-right attributes: the “private green innovation” type, the “private green concentration” type, and the “state-owned incentive decentralized” type. According to the research results, this study suggests that, in the process of promoting the improvement of the business performance of marine resource enterprises, the Chinese government should promote the green development of enterprises, stimulate the technological innovation vitality of private marine resource enterprises, optimize enterprise executive incentive policies, and deepen the reform of mixed ownership in state-owned enterprises. Compared with previous studies, this article presents a fresh perspective on researching marine resource enterprises from a macro perspective and constructs a policy system for improving the business performance of different types of marine resource enterprises, providing valuable reference and guidance for the high-quality development of marine resource enterprises and the overall marine economy.

Eco-efficiency is commonly used as an indicator of sustainability since it expresses the efficiency with which natural resources are utilized to meet people’s needs. Agriculture relies heavily on these ecological resources and by-produces significant environmental burdens, shifting the interest of researchers and policymakers toward the promotion of ecological practices. However, limited evidence exists regarding eco-efficiency across various ecological approaches like low input, conservation, and organic farming. This paper contributes to the existing literature and provides insight into the eco-efficiency of Cretan olive farms managed under different ecological approaches. Olive oil production is vital for the socio-economic sustainability of Mediterranean agriculture, a significant element of the region’s culture, and the basis of the well-known “Mediterranean diet”; therefore, it is crucial to investigate eco-efficient management options for olive farmers. Data Envelopment Analysis (DEA) and a second-stage statistical analysis are employed to estimate the eco-efficiency of olive farms and investigate factors affecting it. Composite indicators for biodiversity, soil, and input management are incorporated in the eco-efficiency model. The results indicate that organic farms achieve the highest eco-efficiency scores, followed by other ecological approaches. Additionally, eco-efficiency seems to be explained by farmers’ dependency on subsidies, commitment to farming activity, and environmental awareness.

Due to persistent technological impacts on ecological efficiency (eco-efficiency) and variations in economic power and resource endowments among regions, considering regional and temporal heterogeneity becomes imperative. Ecosystems, often divided into economic production and environmental governance stages, necessitate a holistic assessment incorporating regional, temporal heterogeneity and stage distinctions. To address potential issues of a technology gap ratio (TGR) exceeding 1 within a two-stage network structure with dual heterogeneity, we introduce a segmented projection three-layer meta-frontier analysis method. In empirical study, we systematically examined eco-efficiency, emissions inefficiency and technology gaps across management, regional and temporal dimensions in 30 Chinese provinces from 2016 to 2020. Findings reveal disparities in management eco-efficiency, with the central provinces outperforming the east. Regional differences indicate advanced technology in the east, contributing to superior eco-efficiency. Temporal analysis highlights the positive role of scientific and technological development. Emissions inefficiency improvements are noted, necessitating attention toward management and regional technology levels. Eastern provinces exhibit superior emissions efficiency, emphasizing the role of regional and technological development. Recommendations include prioritizing environmental governance, strengthening regional collaborations and implementing policies to bridge technology gaps.

This paper aims at exploring the efficiency of regional airports, whose traffic is highly dependent on touristic flows, and the impact of some external factors such as low-cost airlines, charter air traffic and seasonality. The analysis focuses on the airport market in Greece within the time period from 2010 to 2016. A bootstrapped data envelopment analysis model is developed and the Malmquist Productivity Index is computed to estimate the total productivity change between 2010 and 2016. This is followed by a tobit regression model to estimate the impact of the external factors on the airport efficiency scores. Our findings indicate that the considered factors significantly affect airport efficiency. More specifically, the low-cost airlines and charter flights contribute to increasing airport efficiency, while the seasonality might be seen as an obstacle to improving airport efficiency. To the best of our knowledge, this research goes beyond any previous study in the Greek airport market and could be useful for several practitioners, such as airlines, airport operators and hotel businesses, as well as policy-makers and authorities.

The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model.

The main purpose of this study is to provide a comparative analysis of several possible approaches to applying data envelopment analysis (DEA) in the case where some decision making units (DMUs) in the original sample have negative system outputs. In comparison to the traditional model of Charnes, Cooper, and Rhodes (CCR) and the CCR model with a scale shift to measure second-stage outputs, the range directional measure (RDM) model produces the most appropriate results. In this paper, an approach is proposed for estimating returns to scale. The study applies a two-stage DEA model with negative second-stage outputs to assess the public support for research, development, and demonstration projects in the energy sector in 23 countries over the period from 2010 to 2018. The assessment of government performance depends on its contribution to the growth of energy efficiency in the national economy and the reduction of its carbon intensity. Intermediate outputs (patents in the energy sector) are included in the analysis as both outputs of the first stage and inputs of the second stage. Taking the similarity between the calculations obtained without stage separation and the system efficiency calculations from the two-stage model as a measure of model adequacy, the RDM model shows the highest similarity scores.

Measuring and understanding the evolution of the electricity generation efficiency sector is a relevant task for policymakers. This study focuses on assessing the determinants of electricity generation efficiency across 24 countries in Latin America and the Caribbean between 2000 and 2020. A hurdle model was employed to account for the censored nature of the dependent variable. The findings show a positive impact of opening up the electricity sector in fully efficient countries. Market liberalization and the involvement of privately owned power plants contribute to efficiency gains in inefficient countries. Additionally, the tariff protectionism level exhibits positive effects for completely efficient and inefficient countries. These results provide valuable insights, as less-efficient countries can glean strategies from their more efficient counterparts to enhance their electricity generation efficiency.

The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23′ E, latitude 60 52′ N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. The DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels’ energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.

Economic activities’ efficiency represents the level of performance that uses the lowest quantity of inputs to achieve the highest possible amount of output. This paper presents the process of calculating the relative efficiency of separate non-financial activities in an economy using the DEA methodology. The purpose of this paper was to create the DEA model for monitoring the relative efficiency of individual non-financial activities of the economy. The purpose was achieved through the realization of two objectives. The first one included the determination of the relative efficiency of the above-mentioned activities in the period from 2002 to 2020 using the data from non-financial entities in the Republic of Croatia. The second objective consisted of ranking the economic activities according to their relative efficiency. An output variable that measures the efficiency was presented using the return on assets, while the total debt to EBITDA, EBITDA per employee, assets turnover and human capital efficiency were used as input variables. Research results indicate that the DEA methodology could be used as an economic activity’s relative efficiency measurement tool, giving the possibility to rank it according to its relative efficiency using the accounting ratios. Research results show that service sectors’ economic activities were the most efficient ones according to the lower assets engagement and the respective sources of financing that dominate. The highest average relative efficiency in 19 years was scored using wholesale, retail and repair activities as well as information, communication and education. The lowest average relative efficiency was achieved in construction, water supply, sewerage, waste management and remediation activities as well as accommodation and food service activities, which is the consequence of their low level of activity and profitability and high indebtedness in the analyzed period. The relative efficiency scores calculated using the DEA methodology could be used as a benchmark for companies on a micro level, while on the macro level decision-makers can obtain a deeper insight into the relative efficiency of the nonfinancial activities.

Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning models. The results of these models are significantly influenced by the financial features entering them. The aim of this paper was to select the most suitable financial features for bankruptcy prediction. The research sample consisted of enterprises conducting a business within the Slovak construction industry. The features were selected using the domain knowledge (DK) approach and Least Absolute Shrinkage and Selection Operator (LASSO). The performance of VRS DEA (Variable Returns to Scale Data Envelopment Analysis) models was assessed with the use of accuracy, ROC (Receiver Operating Characteristics) curve, AUC (Area Under the Curve) and Somers’ D. The results show that the DK+DEA model achieved slightly better AUC and Somers’ D compared to the LASSO+DEA model. On the other hand, the LASSO+DEA model shows a smaller deviation in the number of identified businesses on the financial distress frontier. The added value of this research is the finding that the application of DK features achieves significant results in predicting businesses’ bankruptcy. The added value for practice is the selection of predictors of bankruptcy for the analyzed sample of enterprises.

Installing solar photovoltaic panels on building rooftops can help property managers generate renewable energy and reduce electricity costs. However, the existence of multiple efficiency indicators and ambiguity in interpreting these metrics limits the comparison of the performance of individual installation projects. This paper presents a methodology using data envelopment analysis to evaluate suitable candidates for rooftop solar panel installation. This approach integrates rooftop area, solar irradiation, temperature, costs, energy yield, and revenue to evaluate the relative efficiency of each building. To demonstrate the methodology, it was applied to rank 22 residential buildings, revealing the top performers for installation in 2022. The approach was subsequently adapted to assess potential outcomes under deferred implementation up to 2030, encompassing a diverse range of climate and pricing scenarios. Five installations were found to be optimal irrespective of the future scenarios. In addition, a super-efficiency approach was applied to overcome the low level of discrimination among the possible installations and to rank each individual unit uniquely. The analysis is designed to guide property owners in identifying favorable solar photovoltaic investments within their portfolios under changing conditions.

Insufficient capital investment coupled with limited land resources significantly limits cultivated land use efficiency (CLUE). China’s rural credit policy system is currently growing, yet the impact of farmer-level credit on CLUE remains understudied. Therefore, this study investigated whether rural credit enhances CLUE by using the China Rural Revitalization Survey (CRRS) data. It explored the impact and mechanism of rural credit on CLUE using least squares regression and mediation models. According to our results, the average CLUE level for farmers in the study area was 0.661, which still had much room for improvement compared with Japan, which also has a large population but limited land. In addition, access to rural credit positively affected CLUE, with cultivated land scale-up and technological applications mediating the rural-credit-driven increase in CLUE. Further analyses of the rural credit allocation mode (RCAM) indicated that farmers’ use of credit for investment in agricultural production factors boosted CLUE more than non-farming allocation of credit funds. These findings suggest that the government should continue to increase support for rural credit while improving the monitoring mechanism for credit allocation to prevent rural credit de-farming from hindering CLUE improvement.

Given that most current linguistic multiattribute decision-making methods do not consider the optimal efficiency of decision-making units and the psychological behavior of decision makers, a linguistic multiattribute decision-making method based on regret theory, data envelopment analysis, and the minimax reference point method is proposed. First, based on the decision-maker psychology of regret and avoidance, the perceived utility value of each decision-making unit was calculated using the language regret–joy value function. The subjective and optimal efficiency values of each decision-making unit were obtained using the subjective weighting and data envelopment analysis methods, respectively. Next, we considered the best efficiency as the reference point and the subjective efficiency as the decision preference to establish a minimax reference point model. By solving the model, a set of public weights that minimizes the difference between the efficiency values of all decision-making units and their optimal efficiency values can be obtained to sort the decision-making units and select the best. The feasibility of the method was verified using an example of employee evaluation and selection; the effectiveness of the method was demonstrated through comparative analysis with other methods.

This study undertakes a comprehensive analysis of vegetable production efficiency in China using input–output data from 30 provinces spanning 2011 to 2017. By incorporating environmental pollution costs as undesirable outputs alongside vegetable output value, we employ Data Envelopment Analysis (DEA) with the Banker, Charnes, and Cooper (BCC) model and the Malmquist index model. Our assessment reveals both annual and inter-period efficiency changes. The findings highlight a modest overall efficiency in China’s vegetable production and significant regional disparities. Technical progress emerges as a pivotal determinant of total factor productivity (TFP). Recognizing these dynamics, we propose policy recommendations that prioritize technical innovation, sustainable practices, rural infrastructure enhancement, and specialized cultivation methods. Implementing these recommendations could bolster China’s position in international trade negotiations due to increased exports and potentially drive broader environmental policy reforms. As vegetable production becomes more efficient and sustainable, there might be a shift in labor needs, potentially leading to migration patterns or changes in employment structures. These insights contribute to the sustainable development of China’s vegetable industry, offering a broader understanding of the dynamics of agricultural efficiency in the context of environmental sustainability.

The present research aims to analyze the efficiency of human capital in relation to sales in each of the subsectors of economic activity within Mexican small- and medium-sized enterprises in the manufacturing industry. To accomplish this, a panel data set covering the years 2009–2020 is utilized. The inputs used are investment in training, salary, and days worked, with sales as the output. Initially, due to the high variability (cv > 1) of both the inputs and the output, the information is divided into three groups by quartiles: Group 1 < 25%, Group 2 = 25–75%, and Group 3 > 75%. As a first step in the analysis, a hypothesis test identifies a significant increase in sales for those subsectors that reported investing in training compared with those that did not. As a result, for the efficiency analysis, SMEs that report not investing in training are removed from the sample. Subsequently, to confirm the statistical explanation of the inputs for the output, a regression analysis is performed. With an input-oriented DEA model, it is found that most subsectors exhibit high overall and pure efficiency (≥0.75) as well as increasing returns to scale. Interestingly, the research introduces a novel approach by proposing subgroups within SMEs, providing a more precise analysis. The findings of this study emphasize the fundamental role of human capital as a key driver of economic growth and innovation within the manufacturing sector. This research also highlights variations in efficiency among different subsectors, underscoring the need for tailored strategies for each. These results offer practical guidance for companies seeking to optimize their operations and contribute to the economic development of a developing country. In conclusion, this paper contributes both theoretically and practically to understanding the interaction between human capital and financial indicators. The results underscore the importance of investing in workforce development, ultimately promoting economic growth, improving productivity, and advancing social progress.

The aim of this study is to solve the problem of increasing the efficiency of fuel and energy complex enterprises. Because such enterprises are complex systems, it is difficult to optimize their work, taking into account all the technical indicators of such enterprises. This study proposes to solve this problem by defining an algorithm using the data envelopment analysis (DEA) method. In particular, the algorithm was applied in heating systems using the example of a combined heat and power plant, where the DEA method had not previously been used. Experiments were carried out to improve the efficiency of the combined heat and power plant. Efficiency indicators were calculated, changing inputs and outputs of the model according to the study case to achieve the maximum efficiency of the system. The Charnes; Cooper and Rhodes; and the Banker, Charnes, and Cooper models were tested with good results. The presented methodology and experimental results enabled the DEA method to be applied for the first time to improve the efficiency of fuel and energy companies.

The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole.

The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA’s status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.

The objective of this study is to evaluate assets’ performance by considering the exit time within the risk measurement framework alongside Shannon entropy and, alternatively, excluding these factors, which can be used to create a portfolio aligned with short- or long-term objectives. This portfolio effectively balances the potential risks and returns, guiding investors to make decisions that are in line with their financial goals. To assess the performance, we used data envelopment analysis (DEA), whereby we utilized the risk measure as an input and the mean return as an output. The stop point probability–CVaR (SPP-CVaR) was the risk measurement used when considering the exit time. We calculated the SPP-CVaR by converting the risk-neutral density to the real-world density, calibrating the parameters, running simulations for price paths, setting the stop-profit points, determining the exit times, and calculating the SPP-CVaR for each stop-profit point. To account for negative data and to incorporate the exit time, we have proposed a model that integrates the mean return and SPP-CVaR, utilizing DEA. The resulting inefficiency scores of this model were compared with those of the mean-CVaR model, which calculates the risk across the entire time horizon and does not take the exit time and Shannon entropy into account. To accomplish this, an analysis was conducted on a portfolio that included a variety of stocks, cryptocurrencies, commodities, and precious metals. The empirical application demonstrated the enhancement of asset selection for both short-term and long-term investments through the combined use of Shannon entropy and the exit time.

Browsing is a prevalent activity on the World Wide Web, and users usually demonstrate significant expectations for expeditious information retrieval and seamless transactions. This article presents a comprehensive performance evaluation of the most frequently accessed webpages in recent years using Data Envelopment Analysis (DEA) adapted to the context (inverse DEA), comparing their performance under two distinct communication protocols: TCP/IP and QUIC. To assess performance disparities, parametric and non-parametric hypothesis tests are employed to investigate the appropriateness of each website’s communication protocols. We provide data on the inputs, outputs, and efficiency scores for 82 out of the world’s top 100 most-accessed websites, describing how experiments and analyses were conducted. The evaluation yields quantitative metrics pertaining to the technical efficiency of the websites and efficient benchmarks for best practices. Nine websites are considered efficient from the point of view of at least one of the communication protocols. Considering TCP/IP, about 80.5% of all units (66 webpages) need to reduce more than 50% of their page load time to be competitive, while this number is 28.05% (23 webpages), considering QUIC communication protocol. In addition, results suggest that TCP/IP protocol has an unfavorable effect on the overall distribution of inefficiencies.

Technical efficiency is considered a useful advisory tool for managers whose main goal is to maximize profit and minimize costs. Data envelopment analysis is a widely accepted methodology for technical efficiency estimation in the sector of agriculture. For that reason and with the view to extract useful conclusions regarding farmers’ effective management of inputs, this study aims to present the DEA method through its implementation in a set of farms located in the regional unit of Pieria. To conduct this analysis, relevant data were collected through a survey in which 40 farms participated. The output variable was chosen to be each farm’s total amount of sales, while the inputs were selected in a way to represent the main factors of production, such as (1) land in acres, (2) labor in hours, and (3) variable costs in EUR. The results showed that the examined farms need to reduce the inputs used by 34.6% to operate more efficiently from the point of view of the CRS model. Therefore, farmers should be motivated to reduce the inputs used, something that can be done through the provision of specialized advisory services. This will, of course, be helped by both the local authorities and the policies of the country in which the rational use of inputs seems to be necessary. This study may contribute to the relevant literature, agriculture, and the area since management suggestions are formulated for the farmers of Pieria’s regional unit.

Reducing the quantities of engineered materials provides a significant opportunity to mitigate the environmental impacts caused by material production and processing. Although the efficient use of materials in building construction has been emphasized, there has been little attention given to measuring the material use efficiency (MUE) of a project. This research fulfills this gap by using data envelopment analysis (DEA) as a benchmarking tool to generate an overall perspective on the MUE and to further compare its efficiency with that of peer projects, thereby promoting enhanced efficiency through target setting. In this research, MUE was measured by adopting the quantities of a variety of materials consumed during construction as input variables and the floor area of a built facility as an output variable. To generate a reliable MUE performance, a stepwise variable selection process was applied and then the performance was ranked based on evaluating cross-efficiency. In addition, clustering analysis and DEA were fused to enable a more realistic target to be set for each input, thereby determining practical targets for each underperforming project. It is anticipated that the proposed MUE benchmarking model would enable projects to recognize the gap with the best-performing projects and help them determine the targets to focus on to become efficient.

Assessing the energy cycle and greenhouse gas (GHG) emissions of wheat production in small Egyptian farms is essential to improve wheat productivity to meet population growth and achieve sustainable development. This study aims to compare wheat production in terms of energy use and GHG emissions for different scenarios in the Delta of Egypt and to use Data Envelopment Analysis (DEA) to optimize the wheat production system. Three common scenarios of the wheat production system (S-I, S-II, and S-III) from old lands with one scenario (S-IV) from newly reclaimed land were included in the study. Data were collected from small farmers through a face-to-face questionnaire and interviews in 2022–2023. The results showed that the third scenario (S-III) in the old lands had the lowest input energy consumption (42,555 MJ ha−1) and the highest output energy (160,418 MJ ha−1), with an energy use efficiency of 3.770. In comparison, the input and output energy for the newly reclaimed scenario (S-IV) were 37,575 and 130,581 MJ ha−1, respectively, with an energy use efficiency of 3.475. S-III was an optimum scenario due to its high energy indicators, such as energy productivity of 0.173 kg MJ−1. The total GHG emissions of S-III were the lowest in old lands with a value of 1432.9 kg CO2-eq ha−1, while S-IV had 1290.2 kg CO2-eq ha−1. The highest GHG emissions input was diesel fuel for machinery and irrigation, followed by manure, chemical fertilizers, and agricultural machinery use. Using mechanization in most farming operations for S-III and S-IV led to decreased losses of agricultural inputs with increasing outputs (yield and straw). Therefore, using them in wheat farming practices is recommended to increase the wheat farming system’s energy efficiency and GHG emissions.

This paper discusses the mixing of polylactide (PLA) and glass fiber which use injection molding to produce a functional composite material with glass fiber properties. The injection molding process explores the influence of glass fiber ratio, melt temperature, injection speed, packing pressure, packing time and cooling time on the mechanical properties of composite. Using the orthogonal table planning experiment of the Taguchi method, the optimal parameter level combination of a single quality process is obtained through main effect analysis (MEA) and Analysis of variance (ANOVA). Then, the optimal parameter level combination of multiple qualities is obtained through principal component analysis (PCA) and data envelopment analysis (DEA), respectively. It is observed that if all the quality characteristics of tensile strength, hardness, impact strength and bending strength are considered at the same time, the optimal process conditions are glass fiber addition 20 wt %, melt temperature 185 °C, injection speed 80 mm/s, holding pressure 60 MPa, holding time 1 s and cooling time 15 s, and the corresponding mechanical properties are tensile strength 95.04 MPa, hardness 86.52 Shore D, impact strength 4.4408 J/cm2, bending strength 119.89 MPa. This study effectively enhances multiple qualities of PLA/GF composite.

The data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on conventional statistical inference, generate inconsistent estimates when dealing with incoherent data-generating processes. This inconsistency arises due to the efficiency scores being limited to the unit interval, and the estimated scores often exhibit serial correlation and have limited observations. To address such inconsistency, several strategies have been suggested, with the most well-known being an algorithm based on a parametric bootstrap procedure using the truncated normal distribution and its regression model. In this work, we present a modification of this algorithm that utilizes the beta distribution and its regression structure. The beta model allows for better accommodation of asymmetry in the data distribution. Our proposed algorithm introduces inferential characteristics that are superior to the original algorithm, resulting in a more statistically coherent data-generating process and improving the consistency property. We have conducted computational experiments that demonstrate the improved results achieved by our proposal.

Across the world, achieving sustainable water resource development and managing limited natural resources like water have proven to be significant issues, and India is no exception to the same. At present, irrigation consumes about 80 percent of the total available water in India and is predicted to remain the dominant water user. India is already a country with water stress and is on its way to becoming a country with water scarcity. Rising agricultural productivity, expanding urbanisation, growing industrialisation, and accelerating industry create competing demands for water. Most irrigation system issues in India are attributable to poor management of the built infrastructure. The present study aims to rank the irrigation canals based on their efficiencies and inefficiencies to improve the overall performance of WUAs in Jharkhand. The ranking is done for nine irrigation canals based on financial inputs like investments in training and development of WUAs, maintenance and repairs of canals, and outputs like users’ charges collection and yield in crop production. The Comprehensive Efficiency score analysis suggests that while there is room for improvement in the performance, the Water Users’ Associations have generally been successful in improving the technical efficiency of the irrigation system. The findings can be used for further investigation to identify best practises and areas for improvement.

The Chinese government clearly put forward a strategy to speed up the development of the digital economy in “the 14th Five-Year” Plan, which will become the booster of China’s development. China has a vast territory and the state of development of the digital economy varies greatly across different regions. It is crucial to clarify the reasons for these differences and take measures to narrow them. Therefore, the evaluation and analysis of the current situation are conducive to the further development of the digital economy. Taking 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan) of China as the research objects, this paper constructs an index system taking digital infrastructure, digital technology and digital talent as input variables and taking digital industrialization and industrial digitization as output variables. The data envelopment analysis (DEA) cross-efficiency model is constructed to calculate and compare the cross-efficiency of the digital economies in each province. The results show the following: (1) The development efficiency of China’s digital economy has generally been low, and there is a large “digital divide” between provinces. (2) The input of digital talents is crucial for the digital economy in order to achieve high output and high efficiency, and high output is often accompanied by high efficiency. Based on the above conclusions, this paper puts forward some suggestions to promote the development of China’s digital economy.

This paper analyzes different lot-sizing policies for the supplier selection and order allocation problem in a two-stage supply chain. The supply chain consists of multiple candidate suppliers and a single buyer. In this system, selected suppliers produce a product in batches at finite production rates, ship it to the buyer, and the buyer sells it to the market at a constant demand rate. Our goal is to evaluate two lot-sizing policies and select the one that optimizes the supply chain by minimizing the total cost and maximizing supplier efficiency. A bi-objective mixed-integer nonlinear programming (BOMINLP) model is proposed. The first objective consists of the development of a coordination mechanism for supplier selection and order allocation that minimizes the entire supply chain cost, and the second objective comprises a data envelopment analysis (DEA) approach to evaluate the overall performance of suppliers to optimize supplier efficiency. Then, the lot-for-lot and order frequency policies are applied to the BOMINLP model separately to determine the set of selected suppliers as well as the corresponding order quantities and number of orders allocated to each selected supplier per replenishment cycle. Numerical examples that illustrate the solution approach and compare the two lot-sizing policies are provided.

To enhance national competitiveness, countries are committed to building a National Human Resource Development (NHRD) system to develop talents. However, studies have rarely investigated the internal development process of the NHRD system and the performance at the system and sub-system levels. Thus, this study constructed the performance evaluation model of the NHRD system from a two-stage process efficiency perspective that first cultivates talents and then uses the talents produced to create value. In addition, considering the problem of international talent flow and the time-lag effect, the bad output and the time-lag between inputs and outputs were incorporated into the model. The subjects included 60 countries, including Argentina, China, and OECD member countries. The results reveal that countries that excel at nurturing talents do not necessarily have the ability to effectively use talents to create value. Only having high-efficiency talent cultivation cannot strengthen competitiveness. Sensitivity analysis was also conducted to identify the input that affects talent cultivation and utilization efficiency, which could be used as a reference for competitiveness and NHRD performance improvement.

In this paper, we propose and compare new methodologies for ranking the importance of variables in productive processes via an adaptation of OneClass Support Vector Machines. In particular, we adapt two methodologies inspired by the machine learning literature: one involving the random shuffling of values of a variable and another one using the objective value of the dual formulation of the model. Additionally, we motivate the use of these type of algorithms in the production context and compare their performance via a computational experiment. We observe that the methodology based on shuffling the values of a variable outperforms the methodology based on the dual formulation. We observe that the shuffling-based methodology correctly ranks the variables in 94% of the scenarios with one relevant input and one irrelevant input. Moreover, it correctly ranks each variable in at least 65% of replications of a scenario with three relevant inputs and one irrelevant input.

We introduce a novel methodological framework based on additive value-based efficiency analysis. It considers inputs and outputs organized in a hierarchical structure. Such an approach allows us to decompose the problem into manageable pieces and determine the analyzed units’ strengths and weaknesses. We provide robust outcomes by analyzing all feasible weight vectors at different hierarchy levels. The analysis concerns three complementary points of view: distances to the efficient unit, ranks, and pairwise preference relations. For each of them, we determine the exact extreme results and the distribution of probabilistic results. We apply the proposed method to a case study concerning the performance of healthcare systems in sixteen Polish voivodeships (provinces). We discuss the results based on the entire set of factors (the root of the hierarchy) and three subcategories. They concern health improvement of inhabitants, efficient financial management, and consumer satisfaction. Finally, we show the practical conclusions that can be derived from the hierarchical decomposition of the problem and robustness analysis.