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• Development of a framework to assess the sustainability of waste management systems • The eco-efficiency index of the systems is assessed using data envelopment analysis • Single MSW management system shows higher efficiency compared to hybrid systems • The amount of food and paper wastes plays an important role in the sustainability • Cogeneration of heat and power system increases sustainability in most scenarios Municipal Solid Waste (MSW) management has become a pressing global issue due to population growth, urbanization, and changing consumption patterns, especially in developing countries. This study develops a comprehensive framework to evaluate the sustainability of MSW management systems. Also, the sustainability of Anaerobic Digestion (AD) and landfill systems with/without various Cogeneration of Heat and Power (CHP) technologies in different conditions using the framework in the municipalities of Iran. The eco-efficiency index, which considers environmental and economic aspects, is used to assess the systems sustainability. The Data Envelopment Analysis (DEA) method is applied to assess and rank the performance of the systems based on their eco-efficiency indices considering parameters such as energy production, costs, emissions, land occupation, water consumption, and employment. The results indicate that the landfill and AD systems with/without CHP have the highest priority for all provinces when the food, paper, and cardboard wastes entering the systems are at their maximum and minimum amounts. However, the systems without CHP have the lowest efficiency when the share of the wastes is 50%. Although the findings can help decision-makers in improving MSW management, there may be limitations to the research, such as data availability and assumptions made during the analysis. [ABSTRACT FROM AUTHOR]

Increasing the price of fossil fuels, unreliability of fossil fuels for a secure supply of demand in future and their relevant environmental concerns provide an attitude toward substituting renewable energies with fossil fuels for reaching sustainable development in societies. Biofuels as a type of renewable energies can be easily transferred between supply chain's centers and do not have limitation for transportation after their production. Among various types of biofuels, biodiesel, which can be mainly produced from the non-edible feedstocks, such as Jatropha Curcas L. (JCL), is preferred to other biofuels because biodiesel production from JCL which can be cultivated in marginal lands, improves three pillars of sustainability. Since biofuel supply chain's costs can mainly be affected by its feedstock location optimization, this paper used a common weight data envelopment analysis (CWDEA) method for location optimization of feedstock cultivation for a biodiesel supply chain by considering a comprehensive set of sustainability criteria for investigating locations. A case study of Iran is provided for assessing the model's application, and its results in ranking potential locations for JCL cultivation are validated by a numerical taxonomy (NT) approach. In fact, this paper not only specifies the optimum locations for the feedstock cultivation of a biofuel supply chain regarding to sustainability criteria, but also discuss the balanced socioeconomic development and environmental benefits which can be attained by JCL cultivation in marginal and mostly underdeveloped lands. Provided results imply that vast area of Iran's marginal lands has suitable climate for JCL cultivation and policy makers can address all aspect of sustainability simultaneously by investment on those specified lands as well as supplying countries' demand for biodiesel which will be produced from this feedstock. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is one of the non-parametric approaches for evaluating efficiency. The present paper investigates a novel recurrent neural network (RNN) model for solving DEA problems. To the best of our knowledge, there is no attempt for this kind of problem using RNN. Thus, this paper is going to show the effectiveness of RNN models for solving DEA problems. As a matter of fact, we give a novel RNN model to solve a general linear programming problem. Moreover, the stability in the sense of Lyapunov for the developed RNN model is proved. Finally, to show the performance of the method we consider a general case model and a case study on Iran's Olympic Sports Caravan and solve with the novel proposed RNN model. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

Purpose: A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran. Design/methodology/approach: In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted. Findings: The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed. Originality/value: The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient's condition becomes more dangerous. [ABSTRACT FROM AUTHOR]

Purpose: A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures. Design/methodology/approach: As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods. Findings: The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software. Originality/value: The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time. [ABSTRACT FROM AUTHOR]

In recent decades, achieving sustainable economic growth and development through energy efficiency has been a key challenge for Persian Gulf countries. This study presents a two-stage analysis of the energy efficiency and environmental performance of Persian Gulf countries in 2000–2014 using data envelopment analysis and Tobit regression. The hypothesis of this study is that energy efficiency is low in the Persian Gulf countries and these countries have the potential to reduce greenhouse gas emissions. At first, using data envelopment analysis, total-factor energy efficiency and environmental efficiency of the Persian Gulf countries were measured. Then, using Tobit regression, the effects of GDP per capita, oil price, industrialization degree, population size, paper citation rate, foreign direct investment, and the degree of commercial openness on energy efficiency were investigated. The results of the first stage measurements show that Saudi Arabia and the United Arab Emirates had the highest and second highest total-factor energy efficiency, respectively, while Oman and Iran had the lowest, and second lowest, respectively. In terms of environmental performance, the UAE and Qatar proved to have the best and second best performance, respectively, while Iran and Iraq showed the weakest and second weakest performance, respectively. The results of Tobit regression revealed that GDP per capita, oil prices, industrialization degree, and population size had a direct relationship with energy efficiency while the paper citation rate (as an index of science, technology, and innovation) and foreign direct investment had an inverse relationship with energy efficiency. This study shows that the Persian Gulf countries could potentially reduce their energy consumption by up to 18%. Finally, a number of environmentally friendly economic policies and several environmental projects are proposed and it is emphasized that more innovative green technologies should be used to increase energy efficiency and optimize the energy structure to combat climate change. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to propose a new purely data-driven method for measuring impact of road safety culture based only on the aggregated data on traffic safety indicators rather than on questionnaire surveys. The paper proposes the use of data envelopment analysis (DEA) with undesirable inputs-outputs for quantitative assessment of the impact on road fatalities of road safety culture. This method determines a number of road safety factors and then distinguishes between the effects of specified factors and the cultural factors. The applicability and usefulness of the proposed DEA are illustrated in a real-world application for assessment of the impact of road safety culture on Iran provincial road fatalities in the time period of two years. Results of DEA enable differentiating between provinces according to the impact of cultural aspects on fatality rate. Moreover, results show that road safety culture on average has improved in the time period of two years. The results of this study may be considered as a basis for traffic safety culture (TSC) interventions at provincial level. This paper provides a new opportunity to look at road safety culture across provincial road transportation systems in Iran and represents a critical step toward understanding relationship between safety culture and safety performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to multi-criteria sustainability performance assessment of horticultural crops. The crops are ranked by the decision-making method ELECTRE IV with environmental, energy and technological criteria. In total eight indicators are taken into consideration and calculated based on primary data collected from over 260 farms in northern Iran. Additionally, Data Envelopment Analysis is used to calculate the technical efficiency and potential for energy saving by different management of the production units. The novel contribution of this study is the comparison of several horticultural products (oranges, kiwis, persimmons and tangerines), when most of the previous studies have focused on one product. Moreover, novel calculations of the carbon footprint are presented for oranges, tangerines and persimmons. This paper also includes the first study on the environmental impact of persimmon fruit's production. The obtained results show that energy efficiency for orange, tangerine, kiwi and persimmon products: 1.1, 0.84, 0.53 and 1.22, respectively. In each hectare of kiwi orchards, the amount of CO2 emissions of 1219 kg and the ecological footprint of 3.21 hectares have been calculated, which is statistically significant compared to orange, tangerine and persimmon. The chemical and fuel inputs have the greatest potential for reducing energy consumption in the studied products. Results of ELECTRE IV showed that kiwi is the most sustainable selection for the studied region followed by orange, persimmon and tangerine, respectively. Kiwi has also relatively low technical efficiency. This means that this product has the greatest potential for a reduction of energy consumption, while maintaining the same amount of crop. It is recommended to include the development of kiwi orchards in the policies of Guilan, but with more careful management of the production inputs. [ABSTRACT FROM AUTHOR]

Petroleum is an important strategic material connected with the national economy's safety. Sustainable supplier performance evaluation plays a considerable role in establishing an effective, sustainable supply chain management (SSCM) and is related to the safety of petroleum production and supply. Social responsibility, governmental regulations, and public consciousness of environmental aspects are enforcing the companies to make their supply chains more sustainable. This paper presents a method for sustainable supplier selection problems by combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), data envelopment analysis (DEA) methods, and Anderson-Peterson rating model. This method proposes a novel procedure for solving the sustainable supplier selection problem. It allows decision-makers to minimize the negative environmental effects and maximize the social impact of the supply chain while maximizing its business performance. At first, the effects between selection criteria are computed by Fuzzy DEMATEL. Then the weights of each criterion-related with to the petroleum supplier selection are derived by the ANP method. Results of this stage are inputs for the next stage, where DEA is applied to select the best suppliers. Finally, 15 petroleum supplier companies in Iran are evaluated and prioritized as a case study to show the capabilities of the proposed model. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) model has been applied for evaluating bank branches and recognizing efficient and inefficient branches can help bank managers to provide appropriate strategies to improve the inefficient branches' performance. Conventional DEA models are based on the 'black box' approach. However, the process of providing services in banks is made up of interactive and interdependent processes. Additionally, some managers tend to incorporate their preferences in evaluation process. In this paper, best worst method (BWM) is used for incorporating decision maker (DM) preferences in two‐stage DEA model. First, BWM model is applied to obtain the weights of inputs, intermediate measures and outputs based on DM's judgement. Second, generated weights are imposed on two‐stage DEA model as additional constraints and a novel bi‐objective BWM‐two stage DEA model is introduced. Finally, the proposed bi‐objective BWM‐two stage DEA model is solved using min‐max approach. To illustrate the capability of proposed model, 45 Agricultural Bank (Agribank) branches in West Azerbaijan province of Iran are evaluated. The branches' processes are considered as two stages 'production' and 'profitability' and efficiency of branches are calculated in each stage. According to the efficiencies of each sub‐stage, branches are divided to four groups and recommendations are made for each group. [ABSTRACT FROM AUTHOR]

In the current world, dealing with some problems with interval data is inevitable. In this case, the methods applied for real data could not be employed. To solve these problems, the modified version of previous methods or new methods should be presented. In this paper, the two-stage ranking method that already has been proposed by the authors is modified to solve the mentioned problems. In each stage, two optimistic and pessimistic attitudes are considered and their corresponding models are presented. Then, an appropriate algorithm for classifying the units based on their obtained interval efficiency is proposed. To demonstrate the applicability of the proposed method, 30 branches of the social security insurance organization in Iran are classified. Also, the validity and consistency of the proposed method are confirmed. The main contributions of this paper are as follows: Decision-making units (DMUs) are ranked with interval inputs and outputs. Inefficiency of the first projection (obtained in the first stage) is applied in the unit rank score. All units are classified in separate classes and all units of each class are ranked. Pareto-efficient projections (practical benchmarks) are obtained for all inefficient units. The proposed model is always feasible and unit invariant. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present two-stage network models in the presence of stochastic ratio data. Design/methodology/approach: Black-box, free-link and fix-link techniques are used to apply the internal relations of the two-stage network. A deterministic linear programming model is derived from a stochastic two-stage network data envelopment analysis (DEA) model by assuming that some basic stochastic elements are related to the inputs, outputs and intermediate products. The linkages between the overall process and the two subprocesses are proposed. The authors obtain the relation between the efficiency scores obtained from the stochastic two stage network DEA-ratio considering three different strategies involving black box, free-link and fix-link. The authors applied their proposed approach to 11 airlines in Iran. Findings: In most of the scenarios, when alpha in particular takes any value between 0.1 and 0.4, three models from Charnes, Cooper, and Rhodes (1978), free-link and fix-link generate similar efficiency scores for the decision-making units (DMUs), While a relatively higher degree of variations in efficiency scores among the DMUs is generated when the alpha takes the value of 0.5. Comparing the results when the alpha takes the value of 0.1–0.4, the DMUs have the same ranking in terms of their efficiency scores. Originality/value: The authors innovatively propose a deterministic linear programming model, and to the best of the authors' knowledge, for the first time, the internal relationships of a two-stage network are analyzed by different techniques. The comparison of the results would be able to provide insights from both the policy perspective as well as the methodological perspective. [ABSTRACT FROM AUTHOR]

Assessing the efficiency of a supply chain (SC) is of great importance for managers and policy makers. For this aim, we propose a network data envelopment analysis (NDEA) model to reflect the internal structure of networks in efficiency evaluation. For many of the real-world performance evaluation problems, data of inputs and outputs are available, and their ratio conveys important messages to managers. However, conventional data envelopment analysis (DEA) models are no longer able to deal with ratio data. This paper aims to extend the NDEA models with the ratio data (NDEA-R) to evaluate the performance of SCs. Therefore, given the internal structure of a supply chain, relationships among different divisions of an SC are determined under two assumptions of free-links and fixed-links. Applicability of the proposed models is illustrated by evaluating supply chain of 19 hospitals in Iran over 6 months. By performing sensitivity analysis, we find out that the overall efficiency score of decision-making units (DMUs) under the fixed link assumption is greater than or equal to the overall efficiency of DMUs under free link assumption. Our proposed model overcomes the underestimation of efficiency and pseudo-inefficiency scores. [ABSTRACT FROM AUTHOR]

Transportation sector with the consumption of 25% of energy play a major role in Iranian economy. This sector produces 27% of total undesirable greenhouse gases in Iran which has directly harmful effects on the environment. Hence, performance assessment of energy efficiency of transportation sector is one of the most important issues for policy makers. In this paper, energy efficiency of transportation sector of 20 provinces in Iran is evaluated based on data envelopment analysis (DEA)—cooperative game approach. First, selected inputs and outputs are categorized into energy and non-energy inputs and desirable and undesirable outputs. Then, classical DEA model is applied to evaluate and rank the provinces. Since, the classical DEA model can't distinguish between efficient provinces, so, this paper ranks the provinces based on combination of cross-efficiency DEA and cooperative game approaches. In the cooperative game theory, each province is considered as a player and the suitable characteristic function is defined for players. Finally, by calculating the Shapley value for each player, the final ranks of transportation sectors in provinces are concluded. The results indicate that some smaller provinces have better energy efficiency in transportation sector in comparison with big provinces. [ABSTRACT FROM AUTHOR]

Benchmarking is a tool for evaluating organizational performance with a learning approach from oth)ers. The importance of benchmarking in every industry is clear for anyone. In the automotive industry, the performance of after-sales service agencies in Iran is evaluated every year by Iran Standard and Quality Inspection company. One of the ways to continuously improve in after-sales service agencies is benchmarking of successful and efficient examples in the network. In this paper, a benchmarking model is developed considering that the repair index and customer satisfaction are interdependent. To improve the accuracy and operationality of benchmarking, some constraints have been added to the model with the opinion of experts. Considering the dependent parameters, a data envelopment analysis model has been proposed and this model has been implemented to benchmark 20 after-sales service agencies of a car company. By solving the model and comparing it with the results of the original model, it was observed that the considered conditions changed the benchmarking and in)creased the accuracy. This paper discusses the concept of the impact and importance of dependent parameters in benchmarking, and with this concept, a benchmarking model for automotive after-sales service agencies is presented. [ABSTRACT FROM AUTHOR]

This study proposes a three-staged approach to data envelopment analysis (DEA) modeling for hospital efficiency. The approach aims to overcome the constraint on the number of inputs/outputs relative to the number of DMUs. Initially, the principal components of all inputs and outputs are determined using principal component analysis (PCA). Next, these principal components enter a factor analysis (FA) process to construct a two-level hierarchy of inputs/outputs and to establish a weighting scheme based on explained variances of components. Finally, a two-level DEA (TLDEA) method is applied to the resultant framework to determine the relative efficiency of hospitals using data from the healthcare context of Iran as an emerging economy. The outcomes of applying the proposed PCA-FA-TLDEA approach are argued to offer a substantial increase in the discriminatory power of classical DEA methods and could incorporate a relatively large set of inputs/outputs already existing in the hospital efficiency literature. As demonstrated in the evaluated hospitals, the PCA-FA-TLDEA methodology improved the discrimination from 0% in the original DEA to 45%. The paper proposes a novel three-stage DEA model by using PCA to extract the principal components from the inputs and outputs; therefore, reducing the number of inputs and outputs and their inter-correlations. Secondly, a hierarchy of inputs and outputs by applying FA to the principal components is constructed. Finally, the TLDEA method to the hierarchy of inputs and outputs is applied to evaluate the performance of public hospitals. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is non-parametric mathematical programming for measuring the performance of a set of homogeneous decision-making units (DMUs). Standard DEA models usually result in several efficient units, so, picking the best unit among efficient units has been one of the most challenging subjects in DEA literature. With reference to various researchers, the common set of weights (CSW) approach has been intriguing among them. This paper discusses a mechanism for detecting a common set of weights which is managed to be always positive and prevents weights dissimilarity. Employing this common set of weights can determine the efficiency score of each unit and finally rank them based on their obtained efficiency score. Equally, the proposed model not only provides the closest targets, but also minimizes the deviations of actual DMUs and extreme efficient units. In order to verify the proposed approach an empirical example of Iranian electricity distribution companies is explained. [ABSTRACT FROM AUTHOR]

Purpose: Analyzing factors of delays in construction projects and determining their impact on project performance is necessary to better manage and control projects. Identification of root factors which may lead to project delay and increased cost is vital at the early or planning stage. Better identification of delay factors at the early stage can help the practitioners to reduce their impacts over the long run. Hence, the purpose of this paper is to propose an intelligent method to analyze causal relationships between delay factors in construction projects. The proposed approach is further validated by a real case study of the construction projects in West Azerbaijan province in Iran. Design/methodology/approach: During the first phase, the fuzzy cognitive map (FCM) is drawn to indicate the causal relationships between the delay factors and the evaluation factors. For this purpose, the causal relationships between 20 delay factors and four evaluation factors are considered. Afterward, the effect of each factor on management goals is evaluated by using a hybrid learning algorithm. Delay factors are further prioritized by applying fuzzy data envelopment analysis (FDEA). In the second phase, an interpretive structural modeling (ISM) is employed to determine the root causes of delay factors. Findings: Results of the first phase show that "supervision technical weaknesses for overcoming technical and executive workshop problems" and "Inaccurate estimation of workload, required equipment and project completion time" are the most significant delay factors. In contrary, "non-use of new engineering contracts" has the lowest impact on the management goals. Meanwhile, the results of the second phase conclude that factors like "Inaccurate estimation of workload, required equipment and project completion time" "weakness of laws and regulations related to job responsibilities" and "lack of foreseen of fines and encouragements in the contracts" are the most significant root factors of delay in construction projects. Originality/value: This paper integrates three methods including FCM method, FDEA and ISM. In the first phase, FCM is drawn according to the experts' opinions and concerning management goals and delay factors. Later, these factors are prioritized according to the results of running the algorithm and using the FDEA model. The second phase, the seven-step in the ISM methodology, is done to identify the root factors. To ensure that the root factors of the delay are at a lower level of hierarchical structure, delay factors are partitioned by drawing the ISM model. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering. Design/methodology/approach: First, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers. Findings: This paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters. Originality/value: The main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors' proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors. [ABSTRACT FROM AUTHOR]

The problem of determining an optimal benchmark to inefficient decision-making units (DMUs) is an important issue in the field of performance analysis. Previous methods for determining the projection points of inefficient DMUs have only focused on one objective and other features have been ignored. This paper attempts to determine the best projection point for each DMU when the inputs and outputs data are in stochastic form and presents an alternative definition for the best projection by considering three main aspects: technical efficient, minimal cost, and maximal revenue as much as possible. Considering the important role of the electricity industry in the economic growth of each country, a practical example has been implemented on 16 regional electricity companies in Iran in 9 consecutive periods. The efficiency score along with the projection points of the three technical models (BCC model of Banker et al. (1984)), cost, and stochastic revenue are compared with the projection point obtained from the model presented in this article, which simultaneously meets these three objectives, showing the improvement of companies' performance. [ABSTRACT FROM AUTHOR]

Benchmarking is a tool for evaluating organizational performance with a learning approach from others. The importance of benchmarking in every industry is clear for anyone. In the automotive industry, the performance of after-sales service agencies in Iran is evaluated every year by Iran Standard and Quality Inspection company. One of the ways to continuously improve in after-sales service agencies is benchmarking of successful and efficient examples in the network. In this paper, a benchmarking model is developed considering that the repair index and customer satisfaction are interdependent. To improve the accuracy and operationality of benchmarking, some constraints have been added to the model with the opinion of experts. Considering the dependent parameters, a data envelopment analysis model has been proposed and this model has been implemented to benchmark 20 after-sales service agencies of a car company. By solving the model and comparing it with the results of the original model, it was observed that the considered conditions changed the benchmarking and increased the accuracy. This paper discusses the concept of the impact and importance of dependent parameters in benchmarking, and with this concept, a benchmarking model for automotive after-sales service agencies is presented. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to emphasize the relationship between managerial ability, earnings management, internal control quality and audit fees to establish whether or not there is a significant relationship between the variables of managerial ability, earnings management, internal control quality and the audit fees. Design/methodology/approach: The study sample includes 190 listed companies on the Tehran Stock Exchange during 2009–2016. Research hypotheses were tested using the statistical methods of multivariable linear regression and data envelopment analysis pattern. Findings: The obtained results indicate that there is a significant and direct relationship between managerial ability and internal control quality as well as real earnings management and internal control quality. Based on the results obtained from the second hypothesis, the authors could claim that there is an inverse and significant relationship managerial ability and audit fees. The third hypothesis also revealed that in companies with lower audit fees, there is a stronger relationship between managerial ability and internal control quality. The results of related tests show no significant relationship between accrual-based earnings management and internal control quality. Originality/value: This paper is the first study in Iran whose main focus is on the relationship between managerial ability, earnings management, internal control quality and audit fees. [ABSTRACT FROM AUTHOR]

Productivity growth and efficiency improvements are the major sources of economic development. Pure efficiency, scale efficiency, and technology are basic factors, and rules and regulations and balance are recently known factors affecting the productivity growth. This paper focuses on the effect of manpower ethics and physical space facilities of bank branches as two factors affecting the Malmquist productivity index in the bank branches evaluation. The proposed model uses assurance region weight restrictions to increase discrimination power of basic data envelopment analysis models for the constant return to scale technologies. The validity of the proposed model and the extended Malmquist index is confirmed with an empirical case study of 74 branches of aspecializedbank in thehousing sector of Iran in two time periods of 2017 and 2018. The results for both traditional and extended indicesand the effects of manpower ethics and physical space facilities are analyzed. [ABSTRACT FROM AUTHOR]

Though increasing food supply in order to meet the rising demand for nutrition is a global social imperative, reducing the dependence on imports of essential food commodities is both an economic and a geo-political imperative for national governments. However, in light of the Sustainable Development Goals, although Zero Hunger (SDG2) and Good Health and Well-Being (SDG3) can be ensured within a country when the inhabitants are well-nourished and staple food items remain affordable to one and all, oftentimes, there are trade-offs in the process, with the environmental dimensions—SDGs 13 (Climate action), 14 (Life below water) and 15 (Life on Land). In this paper, using a combination of Environmental-Life Cycle Assessment (E-LCA) and Data Envelopment Analysis (DEA), the authors have evaluated the eco-efficiency of 169 wheat cultivation systems in the Golestan province in the north of Iran. Benchmarking performance based on the best-performing wheat farms and optimizing (decreasing essentially) the consumption of resources, will enable an average reduction of between 10% and 16% in global warming, acidification, eutrophication, and non-renewable energy usage of the wheat cultivation systems in the case study region. The authors recommend the use of this combination not only for wheat cultivation in other regions of the world, but also for other agricultural systems. [ABSTRACT FROM AUTHOR]

In many countries, including Iran, Provincial Departments of Physical Education try to develop the athletic sports and sports for all in their related areas (state), using the government resources. Their success rate has always been an important subject for the top sports managers of country. In this paper we use data envelopment analysis (DEA) and analytic hierarchy process (AHP) techniques for analyzing the performance of physical education organizations in Iran. Some convex and nonconvex DEA models have been used. Afterwards, we have used the Shannon's entropy for aggregating the results obtained from different models and providing a final efficiency score (FES) and a unified ranking. It can be seen that, in the ranking approach provided in this paper the most productive scale size (MPSS) units have the best rank (see Proposition 1). Our findings reveal that the average of FESs of the states is 0.472635 and 50% of the states have an FES more than this average. Classifying the sates to five efficiency classes, "Excellent, Good, Middle, Weak and Very Weak", the percentage of the states belonging to these classes are 6.7, 30, 16.6, 36.7 and 10, respectively. Also, some correlation and difference studies have been carried out using the Pearson's correlation and student's t-tests. Finally, comparisons between the results of some relevant existing publications and those given in the present paper are addressed. [ABSTRACT FROM AUTHOR]

Given the perennial imbalance and chronic scarcity between the demand for and supply of available organs, organ allocation is one of the most critical decisions in the management of organ transplantation networks. Organ allocation systems undergo rapid revisions for the sake of improved outcomes in terms of both equity and medical efficiency. This paper presents a Data Envelopment Analysis (DEA)-based model to evaluate the efficiency of possible patient-organ pairs for kidney allocation in order to enhance the fitness of organ allocation under inherent uncertainty in such problem. Eligible patient-kidney pairs are regarded as decision making units (DMUs) in a Credibility-based Fuzzy Common Weights DEA (CFCWDEA) approach and are ranked based on efficiency scores. Using a common set of weights for all DMUs ensures a high degree of fairness in the assessment and ranking of DMUs. The proposed model is also the first allocation method capable of coping with the vague and intervallic medical and nonmedical allocation factors by the aid of fuzzy programming. Verification and validation of the proposed approach are performed in two steps using a real case study from the Iranian kidney allocation system. First, the superiority of the proposed deterministic model in enhancing allocation outcomes is demonstrated and analyzed. Second, the applicability of the proposed fuzzy DEA method is demonstrated using a series of data realizations for different credibility levels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Significant increments of energy demand and the need to decarbonize the existing energy systems motivate policymakers to utilize renewable energy resources. Accordingly, this paper suggests a hybrid electricity generation system that operates with wind and municipal solid waste biomass to generate monotonous currents and protect the environment by using a portion of the urban wastes. To select a hybrid power plant location, Z-number data envelopment analysis is employed considering economic, social, environmental, and strategic factors, in addition to reliability of fuzzy data. Furthermore, a routing problem is solved by the particle swarm optimization algorithm to calculate the optimal cost of urban waste gathering. As a case study, the model is applied to thirty-one cities in Iran; Shahrbabak, Meymeh, and Birjand are three cities selected as optimal hybrid power plant locations. Finally, the sensitivity analysis results indicate the importance of globally adopting land cost and distance from power distribution network factors. [ABSTRACT FROM AUTHOR]

The present study aimed to use a non-parametric data envelopment analysis (DEA) to estimate the energy efficiency and greenhouse gas (GHG) emissions in off-season cucumber-producing greenhouses in different farm level management systems in Iran. Data were collected using a questionnaire completed by 83 cucumber producers through face-to-face interviews. The results showed that the energy use rate was 75.1%, 82.6%, and 86.2% in small (0.5--0.9 ha), medium (0.9--2 ha), and large farms (≥ 2 ha), respectively. In all the farm levels, the greatest energy use shares belonged to electricity, chemical fertilisers, and human labour, respectively. The results of the DEA revealed that the technical, pure technical, and scale efficiencies of the large farms were 87.3%, 92.8%, and 97.1%, respectively, which were higher compared to other farm level management systems. The ratio of energy savings was estimated at 5.62% and 2.97% for small and large farms, respectively. The results also showed that electricity, chemical fertilisers, and diesel fuel were the most responsible for the GHG emissions. By optimising the energy use, pollution per one/ha one of offseason cucumbers can be mitigated by 1 614.5 and 1 315.0 kg of CO2/ha in small and large greenhouses; indicating more attention is required in managing the energy inputs in small-sized agro-ecosystems, especially for electricity. [ABSTRACT FROM AUTHOR]

Sugar plants are one of the most energy-consuming industries in Iran; thus, the implementation of measures to improve energy efficiency seems necessary for such industries. This paper investigated sugar plants' efficiency in different stages for implementation process of energy efficiency measures focusing on determining some relations for benchmarking and reaching efficient status. In doing so, the process of implementation of energy efficiency improvement measures is planned on the basis of experts' opinions. Also, for deriving the efficiency of each plant in each of the stages of implementation of measures and the aggregate efficiency of them a multi-stage data envelopment analysis model is formulated. The obtained results from average efficiency indicated that the under evaluation population in the first, second, third, and fifth stages have a good performance and in the fourth-stage managers could not perform successfully. Also, the results of determining some relations for benchmarking showed that to what extent each of the outputs of each stage of the implementation process of energy efficiency measures in the sugar plants should be changed in order to achieve efficient conditions. The distance between the current output and the desirable output indicates to what extent manager has been successful in operation, studies of the rate of change of each of the outputs to achieve the efficient conditions are of the utmost importance in developing suitable strategies. [ABSTRACT FROM AUTHOR]

The Balanced Scorecard (BSC) provides an outlook of an organization's general performance; it integrates financial perspective with other performance aspects, like learning and growth, internal processes, and customer perspectives. The momentous issue, in implementation of BSC, is the proper selection of measures. The main objective of this paper is to introduce a novel approach in an attempt to select the most appropriate measures by integrating BSC and three-stage Data Envelopment Analysis (DEA) model. To achieving this aim, the BSC's measures are utilized as input and output variables of DEA model and the most appropriate measures in each BSC's perspective are determined with interpretation of the efficiency variations in different stages. An experimental example containing six Iranian banks has been investigated to demonstrate the implementation of this approach. The results indicate that increased staff expertise (L2) and high speed services (P2), respectively in stage one and two are appropriate measures. Also, in this study, we cannot judge about the effect of customer satisfaction rate (C2), because the values of this measure are similar in different decision making units (DMUs). The proposed approach in the current paper helps managers to recognize appropriate measures for staff empowerment, internal process improvement, customer satisfaction increase, and organization's financial outcomes improvement. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) can be applied to a net work structure systems. Considering twostage network, this paper im proves a leaderfollower structure. Likewise, it is not possible to reduce the production of undesirable outputs without any cost. The motiva tion of this study is handling a fair treatment of the undesirable outputs in leaderfollower structure. In the presence of the additional inputs in the second stage, this approach searches the proportional abatement in desirable and undesirable factors. Besides, it focuses on the less sacrifice of the desirable products. Moreover, the theoretical contributions of the proposed model have been illustrated in 13 poultry industry and 25 power plants in Iran. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is a non-parametric approach based on linear programming for evaluating the performance of decision making units (DMUs) with multiple inputs and multiple outputs. The lack of the ability to generate the actual weights, not considering the impact of undesirable outputs in the evaluation process and the measuring of efficiencies of DMUs based upon precise observations are three main drawbacks of the conventional DEA models. This paper proposes a novel approach for finding the common set of weights (CSW) to compute efficiencies in DEA model with undesirable outputs when the data are represented by fuzzy numbers. The proposed approach is based on fuzzy arithmetic which formulates the fuzzy additive DEA model as a linear programing problem and gives fuzzy efficiencies of all DMUs based on resulting CSW. We demonstrate the applicability of the proposed model with a simple numerical example. Finally, in the context of performance management, an application of banking industry in Iran is presented for analyzing the influence of fuzzy data and depicting the impact of undesirable outputs over the efficiency results. [ABSTRACT FROM AUTHOR]

This paper extends the Malmquist productivity index by considering an important factor called the 'balance factor'. Although factors such as efficiency, scale and technology have been considered before, here the effect of imposed strategies on decision-making units (DMUs) is considered. For this purpose, we develop a new model to calculate the balance of DMUs and take into account the effect of this factor in the Malmquist index in addition to three previous factors. The objective of defining the balance factor is to compute to what extent DMUs are aligned with strategies defined by policy-makers. An empirical case study illustrates the need for such a new index and compares the traditional and extended Malmquist index. [ABSTRACT FROM AUTHOR]

Although Data Envelopment Analysis (DEA) assumes that inputs and outputs take non-negative real values, in some realworld cases, data are integer-valued. In some situations, rounding a fractional value to the closest integer can lead to a misleading evaluation of efficiency and in some cases may lead to an infeasible projection point. To date, various radial and non-radial models have been presented. This paper proposes a slacks-based non-linear model that guarantees an integer-valued reference point for all integer targets. Also, the reference point of each target is feasible under the proposed model. The lack of a need to round answers to the closest whole value is an advantage of this method. In addition, the results of this model are compared with other models. An example is used to clarify the suggested method. [ABSTRACT FROM AUTHOR]

Assigning realistic weights to targeting criteria in order to synthesize various geo-spatial datasets is one of the most important challenging tasks for mineral prospectivity modeling (MPM). Techniques for multiple criteria decision-making (MCDM), like MPM, are deeply concerned with combining a large-scale exploration dataset into a single evaluation model for localizing prospects of a certain deposit type. In this paper, we develop the data-driven TOPSIS procedure, as a GIS-based MCDM technique for MPM. Because weighting and integrating various exploration evidence layers are influenced by intricacy and vagueness of ore mineralization process, imprecise selection of targeting criteria may reduce the possibility of exploration success. To address this problem, we applied prediction–area plot for prioritizing, recognizing and weighting efficient and inefficient targeting criteria. In addition, normalized density (Nd) index was then used for assigning significant weights to fractal-based discretized classes of each targeting criterion. After recognition of efficient and inefficient targeting criteria, data-driven TOPSIS procedure was adapted based on participation of only efficient targeting criteria as well as all targeting criteria for porphyry-Cu prospectivity in Varzaghan district, NW Iran. For quantitative assessment, a success rate curve for each of the two prospectivity models generated in this study was drawn. The results prove the superiority of the predictive model based on using efficient targeting criteria. [ABSTRACT FROM AUTHOR]

Microalgae is one of the most promising feedstocks for biofuel production because it yields the high content of sugar and oil. In order to help to develop this nascent industry, this paper proposes a mixed integer linear programming (MILP) model for planning and designing a microalgae-based biobutanol supply chain network. The goal of this study is minimizing the fixed cost of constructing required facilities, transportation costs, and operational costs (harvesting, pretreatment, treatment, and energy conversion). This paper considers supply, production, distribution, and addresses a multi-period model. Since the volume of harvested and dried algae cannot be determined accurately, a fuzzy programming approach is employed to address uncertainties. Additionally, a data envelopment analysis (DEA) method is used to reduce the complexity of solving the proposed model. The applicability of the model is evaluated through a real case study of Iran. • An MILP model is proposed for planning and designing a microalgae-based biobutanol supply chain network. • The model minimizes fixed costs of constructing required facilities, transportation costs, and operational costs. • A fuzzy programming approach is employed to address with uncertainties. • A DEA method is used to reduce the complexity of solving the proposed model. [ABSTRACT FROM AUTHOR]

Road safety index is an important indicator that has been recently introduced as a useful tool to measure the quality of life in many countries and cities. Road safety index is a complex index and it has at least three main components, including road user behavior, vehicle safety, and road infrastructure effects. Many researchers have selected studying road performance from road safety index perspective due to its feasibility and applicability. To calculate the road safety index, a novel approach was proposed using data envelopment analysis method. In this paper, the selected road safety indicators are classified into two groups, namely the desirable and undesirable indicators. The new approach was applied for a case study in the roads of East Azerbaijan Province in Iran. Inefficient roads were recognized applying the proposed method, and strategies were suggested to improve the efficiency of these roads. [ABSTRACT FROM AUTHOR]

In real-world applications, some systems are composed of independent production units that use different inputs to produce outputs. The conventional data envelopment analysis model measures the relative efficiencies of a set of decision-making units with exact values of both inputs and outputs. In this paper, we propose an approach to assess parallel production systems with shared sources and bounded interval data, and also we provide a model that focuses on calculating the efficiency of the whole of a system along with the efficiencies of its components. [ABSTRACT FROM AUTHOR]

Performance evaluation and efficiency analysis is considered to be one of the critical responsibilities of the management department. This paper investigates and assesses the efficiency and performance of Iranian insurance companies through nonparametric frontier analysis (FA) models. The two well-known nonparametric FA models, data envelopment analysis (DEA) and free disposal hull, are utilized to separate the efficient companies from the inefficient companies, and two well-known super-efficiency analysis models are utilized to rank the efficient units. For the further analysis, critical inputs are also identified for inefficient companies using DEA sensitivity analysis which is a powerful tool for planning performance improvement project, and the reference sets are also identified as the benchmark pattern for each inefficient unit. Managerial insights and implications of the study are discussed finally. [ABSTRACT FROM AUTHOR]

Purpose As hospitals are the most costly service providers in every healthcare systems, special attention should be given to their performance in terms of resource allocation and consumption. The purpose of this paper is to evaluate technical, allocative and economic efficiency in intensive care units (ICUs) of hospitals affiliated by Yazd University of Medical Sciences (YUMS) in 2015. Design/methodology/approach This was a descriptive, analytical study conducted in ICUs of seven training hospitals affiliated by YUMS using data envelopment analysis (DEA) in 2015. The number of physicians, nurses, active beds and equipment were regarded as input variables and bed occupancy rate, the number of discharged patients, economic information such as bed price and physicians' fees were mentioned as output variables of the study. Available data from study variables were retrospectively gathered and analyzed through the Deap 2.1 software using the variable returns to scale methodology. Findings The study findings revealed the average scores of allocative, economic, technical, managerial and scale efficiency to be relatively 0.956, 0.866, 0.883, 0.89 and 0.913. Regarding to latter three types of efficiency, five hospitals had desirable performance. Practical implications Given that additional costs due to an extra number of manpower or unnecessary capital resources impose economic pressure on hospitals also the fact that reduction of surplus production plays a major role in reducing such expenditures in hospitals, it is suggested that departments with low efficiency reduce their input surpluses to achieve the optimal level of performance. Originality/value The authors applied a DEA approach to measure allocative, economic, technical, managerial and scale efficiency of under-study hospitals. This is a helpful linear programming method which acts as a powerful and understandable approach for comparative performance assessment in healthcare settings and a guidance for healthcare managers to improve their departments' performance. [ABSTRACT FROM AUTHOR]

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased. • This paper adopted a resource allocation model in DEA with undesired outputs. • Our resource allocation model is sensitive to uncertainty of the data. • A stochastic resource allocation model is given when we have random data and undesirable products. • An application on 21 electricity production & distribution companies for eight years is given. [ABSTRACT FROM AUTHOR]

In standard data envelopment analysis (DEA), it is assumed that inputs of a specific production period are used to generate outputs of the same period. However, in some practical examples, time-lag effects exist between inputs and outputs. The inputs of one period are used to generate outputs for several periods, or inputs of several periods are used to create outputs for one period. In this paper, we present some new DEA models for performance assessment of network production systems with time-lag effects. An empirical application in the horticulture sector in Iran shows the usefulness and capabilities of our proposed approach. [ABSTRACT FROM AUTHOR]

Background: Medical diagnostic laboratories are an essential work environment that plays an important role in diagnosing, treating, and being sensitive to diseases. One way to evaluate laboratories' performance is to calculate their efficiency. This study investigates the efficiency of laboratories that are related to health centers in the south of Iran. Methods: This study was conducted in 2021. The input numbers include: the number of technical personnel and the number of cell counters, and the output data includes: the scores obtained from the level 2 health laboratory evaluation list. And efficiency was calculated with DEAP software. The analysis is accomplished by the assumption of input-oriented. Findings: The efficiency of laboratories of Orzueeyeh and Ravar Cities had the highest efficiency with the assumption of variable returns to scale efficiency 1, and the model of all laboratories is the laboratory of Ravar City. The laboratories of Kuhbanan and Rabor cities had the lowest efficiency with the assumption of variable returns to scale efficiency of 0.859 and 0.899, respectively. The average scale efficiency, Variable returns to scale, and constant returns to scale for laboratories in the cities of Kerman province are 0.842, 0.943, and 0.895, respectively. Conclusions: To increase the efficiency of laboratories, significant resources and funds should be used, as well as few studies have been done on the efficiency of laboratories, which requires more attention. [ABSTRACT FROM AUTHOR]

Background: Health Complex Model was implemented to provide primary health care services in urban, especially in slum areas. As a pilot at a provincial level, Chamran Health Complex offers healthcare for more than 57,000 residents of Tabriz. Despite the necessity of cost information in healthcare decision-making, there was limited knowledge about the unit cost of services. This study aims to analyze the cost and efficiency of health centers. Methods: Activity-Based Costing method with direct and step-down allocation methods was adopted. We estimated unit costs in a hypothetical scenario according to national standards to quantify the gap between current and standard practice. Input-oriented Data Envelopment Analysis was administered to measure the efficiency of health centers. Results: The total cost of the complex was $2,841,897, of which 67% ($1910373) and 33% ($931523) were accounted for direct and indirect costs, respectively. The vaccination center had the lowest ($9), and the occupational health center had the highest average unit cost ($76). The average technical efficiency of the health centers was 0.519, where the HC1 and HC3 showed the best performance. Conclusion: There is remarkable variability in service costs across health centers, which must be addressed in performance management and contracting practices. Although we found a gap between current and standard practice in terms of staff and facilities according to national standards, Chamran Health Complex has an untouched capacity that can be utilized with better planning and without incurring additional costs. It raises the need for revising national standards by the Iran Ministry of Health. [ABSTRACT FROM AUTHOR]

When the costs of the inputs and outputs of the units under evaluation are known, the evaluation of the profit efficiency of the units is one of the most significant evaluations that can provide valuable information about them. In this research, first, a new definition of the optimal scale size based on the maximization of the average measure of profit efficiency is presented. The average measure of profit efficiency develops the concept of economic efficiency measure by introducing a more accurate measure of efficiency compared to the measure of comparative and profit efficiency. It has been shown that the average measure of profit efficiency in a convex space is equivalent to the measure of profit efficiency in constant returns to scale technology, and then, some models are presented to calculate profit efficiency in a stochastic environment, to increase the ability of profit models in real examples by considering the calculation errors of inputs and outputs. Finally, the proposed method is used in an empirical example to calculate the average profit efficiency of a set of postal areas in Iran. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to develop a systematic approach to predict service quality dimension's influence on service quality using a novel analysis based on data envelopment and SERVQUAL. Design/methodology/approach To assess hospital service quality in Tehran, expectation and perception of those who received the services were evaluated using SERVQUAL. The hospital service quality dimensions were found by exploratory factor analysis (EFA). To compare customer expectation and perception, perceived service quality index (PSQI) was measured using a new method based on common weights. A novel sensitivity approach was used to test the service quality factor's impact on the PSQI. Findings A new service quality dimension named "trust in services" was found using EFA, which is not an original SERVQUAL factor. The approach was applied to assess the hospital's service quality. Since the PSQI value was 0.76 it showed that improvements are needed to meet customer expectations. The results showed the factor order that affect PSQI. "Trust in services" has the strongest influence on PSQI followed by "tangibles," "assurance," "empathy," and "responsiveness," respectively. Practical implications This work gives managers insight into service quality by following a systematic method; i.e., measuring perceived service quality from the customer viewpoint and service factors' impact on customer perception. Originality/value The procedure helps managers to select the required service quality dimensions which need improvement and predict their effects on customer perception. [ABSTRACT FROM AUTHOR]

Paint shops are considered as bottlenecks in many automobile companies. As all processes in the paint shop are involved with chemical materials, time is really crucial in the production process, so offering instant remedial actions is crucial. This paper optimizes an online simulation (OS) model, using Discrete-Event Simulation (DES), applied to a paint shop in the automotive industry. To this aim, an integrated Box–Behnken design (BBD) and cross-efficiency data envelopment analysis (DEA) under a neutrosophic environment have been implemented. The former has generated cost-effective scenarios with the minimum number of experimental design, and the latter has provided the efficiency of each scenario enabling to obtain a unique weight for each decision-making unit (DMU) using aggressive and benevolent models as well as a general representation of the human perception toward risks arising from uncertain information, leading to determine the optimal scenario. The proposed approach has been implemented in an automotive industrial plant in Iran, and the results have shown that this approach, compared with previous studies, is a practical way for online monitoring and optimizing the paint shop. [ABSTRACT FROM AUTHOR]

The inverse Data Envelopment Analysis (DEA) method is primarily used to analyze the changing relationship between the inputs and outputs of production units while preserving efficiency levels. This paper aims to perform an efficiency and post-efficiency analysis of 130 public hospitals in Iran. However, we faced with some hospital data that are only available in form of ratios. This motivates us to develop a novel and proper inverse DEA methodology capable of dealing with such cases. We develop the theoretical foundation of the inverse DEA-R model as a post-efficiency analysis approach. Both input-oriented and output-oriented inverse models are developed. In the end, we apply the proposed models for efficiency assessment and moreover inverse analysis of 130 public hospitals in Iran. • In this paper, data envelopment analysis models for ratio data are presented. • Using the concept of inverse data envelopment analysis, models are proposed to estimate ratio inputs and/or ratio outputs. • The feasibility and performance of the proposed models have been proven. • The proposed models are used to estimate the criteria of 130 hospitals in Iran. [ABSTRACT FROM AUTHOR]