Hybrid Data Envelopment Analysis Research Articles

The eco-efficiency of fisheries in EU countries

The main goal of this article was to (1) assess the dynamics of eco-efficiency of fisheries in EU countries and its components and (2) identify potential sources of inefficiencies and efficiency surpluses through slack analysis. The hybrid data envelopment analysis (DEA) model was used for the 2008-2019 period. Progress in eco-efficiency was found among 11 countries (out of 23), but the average eco-efficiency index for the sample was 0.988. Differences in the levels and dynamics of eco-efficiency between the studied countries were mainly driven by the efficiency change component, i.e. internal factors. The largest input-saving potential was found in relation to number of employees and gross tonnage of the vessel, suggesting that sample countries deal with the problem of overinvestment and overstaffing. We also found that greenhouse gas emissions could be reduced by approximately a third.

A Hybrid Data Envelopment Analysis–Random Forest Methodology for Evaluating Green Innovation Efficiency in an Asymmetric Environment

The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development.

Contextual Comparative Analysis of Dar es Salaam and Mombasa Port Performance by Using a Hybrid DEA(CVA) Model

Background: This research conducts a contextual comparative analysis between Dar es Salaam and Mombasa ports, employing a hybrid data envelopment analysis (DEA) model that integrates the contextual value-added approach (CVA). The assessment incorporates various inputs (quay length, number of cranes, and storage area) and outputs (number of ship calls and cargo throughput) to compute efficiency scores, offering nuanced insights into the strengths, weaknesses, and areas for improvement of both ports. Methods: The hybrid DEA model with CVA is applied to calculate efficiency scores, considering the diverse inputs and outputs. This approach allows for a comprehensive evaluation of the relative performance of Dar es Salaam and Mombasa ports. The study also explores the influence of trade-related externalities on port efficiency, providing a holistic understanding of the factors shaping the ports’ operational effectiveness. Results: The efficiency scores depict distinctive performance trends between Dar es Salaam and Mombasa ports. Notably, Dar es Salaam exhibits maximum efficiency (efficiency value of 1) in 2018 and 2021, while Mombasa attains optimal performance (efficiency value of 1) in 2021. However, efficiency values fluctuate for both ports in other years, ranging between 0.895 and 0.985 for Mombasa and 0.924 and 0.960 for Dar es Salaam. Conclusions: This study highlights the dynamic efficiency levels of Dar es Salaam and Mombasa ports over multiple years and identifies critical factors influencing their performance. The findings contribute valuable insights to the field of port analysis, offering guidance to port management and policymakers in optimizing the efficiency and competitiveness of these vital maritime hubs.

Unveiling the potential of hotel mergers: A hybrid DEA approach for optimizing sector-wide performance in the hospitality industry

Mergers and acquisitions (M&A) in the hospitality sector involve consolidating assets among hotels through partnerships. While data envelopment analysis (DEA) has been widely used for hotel efficiency analysis, little attention has been paid to hotel M&A. In this paper, a hybrid DEA methodology consisting of two stages is proposed to identify optimal matches among hotels to enhance sector performance. The initial stage employs an inverse Data Envelopment Analysis (IDEA) model to evaluate the maximum gains that could potentially be generated from pairwise consolidations among hotels. A DEA procedure that incorporates a standard DEA model and a greedy heuristic is developed in the second stage to identify the optimal pairs of hotel mergers. The optimal merger strategy for the entire hotel industry is determined from the complete set of hotels under consideration.The pertinence of the proposed methodology is shown through a sample of 58 hotels from the Sultanate of Oman

Evaluation of Green Innovation Efficiency in Chinese Provincial Regions under High-Quality Development and Its Influencing Factors: An Empirical Study Based on Hybrid Data Envelopment Analysis and Multilevel Mixed-Effects Tobit Models

In the context of China’s high-quality economic development, in-depth research on green innovation efficiency and its influencing factors is crucial for promoting economic transformation and energy conservation. This study employs the Hybrid Data Envelopment Analysis (DEA) method to measure the green innovation efficiency of 30 provinces in China from 2013 to 2019. Subsequently, based on the Multilevel Mixed-Effects (MME) Tobit model and a spatial econometric model, the study investigates the factors influencing green innovation efficiency under the backdrop of high-quality development, and conducts various robustness tests from different perspectives. The results indicate the following: Firstly, the overall level of green innovation efficiency in China is relatively low, but it shows a steady growth trend, with significant differences in green innovation efficiency among provinces in different stages of high-quality development. Secondly, the level of digital economic development, optimization of industrial structure, scale of knowledge dissemination, and degree of openness to the outside world have significant positive effects on green innovation efficiency. On the other hand, the scale of technological innovation, degree of environmental regulation, and guarantee of green innovation have significant negative effects, and the low quality of technological innovation hinders the improvement of green innovation efficiency. Thirdly, the new factors emerging under the backdrop of high-quality development exhibit certain spillover effects on green innovation efficiency. The green innovation efficiency of a province may be influenced by relevant factors in neighboring provinces. This provides new insights for provinces to enhance their green innovation efficiency. The contribution of this study lies in the incorporation of newly emerged factors in the context of high-quality development into the evaluation framework of green innovation efficiency. It accurately measures the green innovation efficiency of each province in China and, based on the analysis of influencing factors, provides novel insights for enhancing green innovation efficiency across provinces.

A hybrid multi-attribute decision-making and data envelopment analysis model with heterogeneous attributes: The case of sustainable development goals

This study presents an integrated multi-attribute decision-making (MADM) and data envelopment analysis (DEA) framework for solving problems with heterogeneous attributes. We classify the heterogeneous attributes into desirable and undesirable classes and provide a model for aggregating the attributes’ weights and the alternatives’ scores. The proposed model is initially designed as a Multiple Objective Decision Making (MODM) problem with a Data Envelopment Analysis (DEA) policy and then reformulated as a linear programming model tackled through a goal programming approach. We apply the proposed model to a set of European countries based on their fulfillment of the 17 Sustainable Development Goals (SDGs) defined by the United Nations. We show the proposed approach minimizes computational efforts and complexities and maximizes the participation and satisfaction of decision-makers. We compare the rankings derived from our model with those obtained from standard MADM techniques such as Euclid and TOPSIS. We illustrate how the different normalization methods are applied to condition the discrimination power of the models and analyze the reversals triggered by TOPSIS relative to the other techniques. We conclude by noting that our model does not rely on the weights defined by the experts to determine the ranking, which constitutes a significant advantage over the standard MADM techniques in strategic evaluation environments.

Can a pursuit of productivity be reconciled with sustainable practices in small-scale farming? Evidence from central and eastern Europe

Small farms constitute the vast majority of agricultural holdings in the world. Therefore, there are the questions of how the small farm sector should evolve and whether economic and environmental goals can be pursued simultaneously. The main objective of this article is to identify potential improvements (a non-radial inefficiency slack) in small farms in Central and Eastern Europe with different types of farming under an environmentally adjusted production function. Based on this, potential development pathways for small farms are assumed. A hybrid data envelopment analysis meta-frontier super-efficiency model with environmental proxies reflecting biodiversity (i.e. crops diversity, grassland, orchards, vineyards) and undesirable outputs (such as soil organic matter loss and GHG sources) and an uncontrollable policy input is used on a country-representative sample of 2320 small farms in four countries: Poland, Romania, Serbia, and Moldova. We found that the more technically efficient small farms are also usually more sustainable when socially desirable criteria were considered. Crops small farms can evolve in two directions: “landscape guardians” and “artisanal (traditional) framers.” Livestock farms could either maintain the status quo or choose an exit pathway. Mixed farms are likely to become landscape guardians, while a sustainable intensification path is open for 20% of farms that specialize in permanent crops.

Efficiency Management of Women Poultry Farmers Using Hybrid DEA and Machine Learning Approach: A Case of SHG-based Production in Sub-Himalayan North Bengal

Data envelopment analysis (DEA) offers a linear programming approach to evaluate the efficiency in diverse fields of production and service sectors with wide utilization for effective performance measurement operations. DEA has found its useful applications in agriculture to examine optimal resource use for sustainable consumption. The popularly used two-step process where DEA is employed along with a regression model to explain the impact of exogenous factors on efficiency has been employed in past studies. This article aims to combine the conventional DEA approach with machine learning (ML) models for establishing a novel alternative method for performance measurement as well as the prediction of key exogenous factors affecting the efficiency of the women self-help groups (SHGs) led poultry farmers in sub-Himalayan North Bengal surrounding the Siliguri region of Darjeeling district. For this purpose, in the first step, DEA was employed to measure the efficiency of 80 women poultry farmers belonging to 20 SHGs and in the second step, the state-of-the-art random forest (RF) technique has been employed to predict the most important efficiency influencing variables. The results suggested inefficiencies among the SHG women with wide variation between the efficient and inefficient units. The use of the RF model predicted important factors such as the role of non-governmental organizations, educational level, financial inclusion, landholding and poultry rearing experience in years to impact the efficiency of these women farmers. As a result, the hybrid DEA-ML approach is useful to tackle ill adversities in poultry production that may help the women SHGs to develop agriculture-based income.

An assessment model for national sustainable development based on the hybrid DEA and modified TOPSIS techniques

Assessing and improving energy efficiency is crucial for addressing the problems of climate change and global warming. This study evaluates the performance of each Organisation for Economic Cooperation and Development (OECD) nation based on the weights obtained by Data Envelopment Analysis (DEA), combined with a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method based on the concept of the aspiration level (called modified TOPSIS-AL). Objective weights are obtained for each country through DEA. This method offers an effective improvement over the previous methods which only use the same subjective weights for the calculation. In addition, our model is also able to account for negative environmental externalities generated during development and provides a comprehensive assessment system for the evaluation of national sustainable development. The findings indicate that the member nations are gradually adopting policies to reduce fossil fuel consumption. However, regional analysis showed that the overall performance of the G7 nations differed significantly from that of the non-G7 nations. The importance of this study lies in the fact that energy consumption not only creates environmental burdens but also affects resource production patterns and productivity. By evaluating the performance of nations in terms of energy efficiency and environmental impact, we aim to address the challenges posed by energy consumption for sustainable development. The proposed hybrid model offers several advantages, including a comprehensive assessment system, consideration of negative externalities, and practical policy recommendations.

A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes

An e-bike sharing system (e-BSS) solves many of the shortcomings of BSS but requires high financial investments compared to BSS. This article proposes a sustainable and targeted extension of the existing BSS with e-bikes and charging piles. The existing BSS in the selected city area is divided into sub-areas using the Voronoi diagram and reference points (landmarks). Then, the integrated approach of the Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) is used to assess the adequacy of the existing bike-sharing stations for updating with e-bikes and charging piles. The joint approach allows decision-makers to look at the whole process and highlight the link between the criteria assessment and user preferences in the context of the chosen reference point. This can encourage future users to use e-BSSs. Based on a thorough literature review, the defined system of criteria takes into account all dimensions of sustainability: the requirements of most stakeholders and the structural features and needs of e-BSS. Finally, the super-efficiency DEA is used to classify the suitable candidates for bike-sharing so that only the most suitable stations are updated. The test of the proposed algorithm in Ljubljana city centre confirms several suitable options for updating the BSS, depending on the reference point.

Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach

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.

Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets

We propose the meta-frontier hybrid data envelopment analysis (MHDEA) model, a non-parametric methodology, to assess the total factor carbon performance index (TCPI) of China's fossil fuel power plants from 2005 to 2015. The model allows for both proportional and non-proportional variables within the production technology. In contrast to conventional DEA models, the MHDEA model simultaneously incorporates the characteristics of group heterogeneity, undesirable outputs as well as hybrid measures. In accordance with the definition of the Malmquist-Luenberger (ML) index, we further explore the driving factors of productivity progress, including efficiency change (EC), best-practice gap change (BPC) and technology gap change (TGC). We employ the difference-in-differences (DID) strategy by combining the variations across regions and years to identify the effect of the 12th Five-Year Plan (FYP) energy saving targets on the TCPI of power plants. Frist, we find that the average TCPI of power plants is 0.50 and the average growth rate is 3.58% per year. Second, the productivity progress of the central group is driven by the innovation effect, while the local group stems from the catch-up effect. Third, the energy intensity reduction target is 1% above the average value and the TCPI is expected to increase by 0.32%. We suggest that the Chinese government design different policies for the central and local power plants based on this heterogenicity.

Analyzing the Investment Behavior in the Iranian Stock Exchange during the COVID-19 Pandemic Using Hybrid DEA and Data Mining Techniques

The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year.

Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach.

This paper proposes a two-step approach to build portfolio models. The first step employs the Data Envelopment Analysis (DEA) to select assets attaining efficient financial performance according to a set of indicators used as inputs and outputs. The second step builds interval multiobjective portfolio models to obtain the optimal composition of efficient portfolios previously identified with respect to investor preferences. The usefulness of this proposed methodology is illustrated through a selected sample of diversified Exchange Traded Funds (ETFs) operating in the US energy sector. Our results with respect to all models and time horizons mainly show that: (i) ETFs related to nuclear energy are more often viewed as efficient according to all DEA models considered; (ii) the efficient portfolios do not contain any ETFs related to the renewable energy sector; and (iii) natural gas and oil are the sectors that have the most ETFs represented in efficient portfolios.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10479-021-04323-6.

Hybrid model for a cross‐department efficiency evaluation in healthcare systems

This work investigates a new hybrid data envelopment analysis (DEA) and grey relational analysis (GRA) for a macro‐level evaluation of healthcare organizations. Once similar processes and decision‐making units (DMUs) are identified, the DEA is used to extract the efficient DMUs and introduce potential improvements. Then, the identified DMUs and inputs are scanned by the GRA to highlight their preference order and thus retrieve the benchmarked DMUs and the most influencing variables to prioritize. An empirical analysis evolving 63 DMUs across four departments of a Tunisian teaching hospital was conducted to provide useful guidance to policymakers to better allocate available resources.

A hybrid data envelopment analysis\u2014artificial neural network prediction model for COVID-19 severity in transplant recipients

In an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)—Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model.

Efficiency Assessment of Seaport Terminal Operators Using DEA Malmquist and Epsilon-Based Measure Models

Today, over 80% of global trade is seaborne. In a world of global supply chains and complex industrial development processes, seaports and port operators play an integral role of utmost importance and act as an incentive to the development of the marine economy and particularly, the national economy in general. Most importantly, the supply chain and demand shocks of Covid-19 on container ports and the container shipping industry have intensified competition among terminal operators. Thus, it is imperative that managers evaluate competitiveness by measuring their past and current performance efficiency indexes. In so doing, we present a hybrid data envelopment analysis (DEA) model that combines the DEA Malmquist method and the epsilon-based measure (EBM) for the first time to address the issue of performance evaluation of seaport terminal operators. The applicability of the proposed hybrid approach is illustrated with a case study of the top 14 seaport companies in Vietnam. First, the Malmquist model is used to assess the total productivity growth rates of the companies, and its decomposition into technical efficiency change (catch-up) and technological investment (frontier-shift). Second, the EBM model is used to calculate the efficiency and inefficiency score of each company. Besides indicating the best-performing companies from certain aspects during the research period (2015–2020), the results reflect that the gap of applying the EBM method in the field of the maritime industry was successfully addressed, and together with the Malmquist model, the integrated framework can be an effective and equitable evaluation model for any area. Furthermore, the managerial implication provides a useful guideline for practitioners in the maritime sector in improving their operational efficacy and helps customers in selecting the best seaport companies in the outsourcing strategy.

Are the Portuguese public hospitals sustainable? A triple bottom line hybrid data envelopment analysis approach

AbstractThe harmonious interaction between humans and the biosphere determines how sustainable existence and coexistence are. However, when social challenges appear, sustainability is heavily compromised. Health is one of the areas affected by such challenges, with the delivery of health services being ensured by health systems. Consequently, understanding how sustainable health services are, especially those that consume the majority of resources—hospitals—is indispensable for a sustainable future. For this reason, we propose using a hybrid data envelopment analysis (DEA) approach to study hospital sustainability in Portugal under environmental, social, and economic perspectives, in cooperation with the Portuguese Ministry of Health. In particular, the proposed methodology incorporates the preference information of decision makers (via the construction of utility scales and the determination of Möbius coefficients) and criteria interactivity, due to the integration of the Choquet multiple criteria preference aggregation model in the DEA approach. In the end, despite approximately 30% of the sampled 29 assessed hospitals were deemed as efficient across the three perspectives in 2018, only 1 was entirely sustainable.

Economic pricing of complex products in a competitive closed-loop supply chain network under uncertainty: A case study of CoPS industry

The development of technology, globalization of the economy and the unpredictable behavior of customers have eventuated in a dynamic and competitive environment in the complex product systems (CoPS) market. Besides, CoPS economic pricing is one of the key factors that dramatically reduces production costs and increases competitiveness. In this regard, this paper unveils a hybrid data envelopment analysis (DEA)-fuzzy mathematical model for economic pricing of CoPS in a competitive closed-loop supply chain network under uncertainty. In the first stage, different CoPS suppliers are evaluated exploiting a DEA model based on a set of economic, technical, and geographical criteria. The advantage of this evaluation is choosing appropriate suppliers, and reducing the complexity of the original model. Next, using a robust optimization model, the strategic and tactical decisions are simultaneously determined, providing a fully optimal solution to the model. In the concerned model, the costs and capacities of facilities are considered to be hemmed in by uncertainty. Eventually, to evaluate the proposed approach, a case study is conducted to derive the important managerial results. The numerical results corroborate that the presented robust model is capable of providing a stable structure under different realizations.

Many-objective x-bar control chart using hybrid NSGA-III and DEA

The present study aimed to find the optimal design of the x-bar control chart which optimises quadruple objectives. The objectives are the average run length, the detection power, the average time to signal, and the expected cost per hour. In this study, a many-objective mathematical model was developed to find the optimal design and solved it using three different variations of the third version of the non-dominated sorting genetic algorithm, named NSGA-III, θ-NSGA-III, and NSDGA-III. The third one is the contribution of this paper which hybridised the NSGA-III with data envelopment analysis (DEA) and technique for order of preference by similarity to ideal solution (TOPSIS). The performances of the three algorithms were tested using a numerical example. The results revealed that the developed NSDGA-III is well defined to explore the search area and in each generation, it is always the best one in choosing solutions that have higher efficiency. Therefore, it is superior in comparing with other algorithms to find better solutions. Hence, the new hybrid algorithm can outperform NSGA-III and θ-NSGA-III. It can be used in all processes with variation due to an assignable cause.