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In conventional DEA models it has been assumed that each measure status is considered input or output. However, a performance measure in some cases can have input role for some DMUs and output role for others and is known as flexible measure. In this paper new slacks-based FNSBM models are proposed in general two-stage network DEA to determine the relative efficiency of units and the role of flexible measures. Then new radial FNDEA-R models and new slacks-based FNSBM-DEA-R models are developed in the presence of flexible measures based on the ratio of input components to output components or vice versa in the input and output orientation under constant returns to scale in general two-stage network. In our proposed models, flexible measures are determined as input or output to improve performance to maximize the relative efficiency of the DMU under evaluation. The FNDEA-R and FNSBM-DEA-R models versus FNSBM models prevent efficiency underestimation and pseudo inefficiency issues. The status of one flexible measure in the input-oriented and output-oriented FNDEA-R and FNSBM-DEA-R models may have different conclusions. The radial FNDEA and FNDEA-R models have unitsinvariant and the objective function of the FNSBM and FNSBM-DEA-R models are invariant with respect to the units of data. A numerical example is used to illustrate the procedures. [ABSTRACT FROM AUTHOR]

The distribution of hospital beds can be used as an indicator to assess the distribution of health services. This study aimed to determine and prioritize indicators of hospital bed allocation in Iran. In the first phase of the study, we used a scoping review to identify criteria affecting hospital bed allocation. Then, we applied an expert panel and the Delphi method to localize the criteria. Finally, we carried out Analytic Network Process method based on Decision Making Trial and Evaluation Laboratory technique (DANP), to determine the weight and importance of indicators and to evaluate the causal relationship between them. Based on the scoping review, we identified 75 criteria that influence bed allocation. In 2 Delphi cycles, 28 criteria were selected and included in the DANP phase. The population, the presence of a reference hospital in the region, the type of hospital, the common diseases in the region, and the presence of home care services were the variables that had the most interaction with other variables. The variable of home care services was at the highest level of the causal chain. Bed occupancy rate, average length of stay, waiting list, as well as type and level of the hospital had the highest priority in the allocation of hospital beds. Furthermore, given the importance of home care services, policies should be adopted to expand these services nationwide. The indicators identified in this study will be useful for the development of policies aimed at an equitable allocation of hospital beds. Key Points: The authors identified 75 criteria affecting the distribution of beds through scoping review.The criteria with the highest level of interaction were population, existence of a reference hospital in the area, the type of hospital, common diseases in the area, and existence of home care services.The variable of home care services was at the highest level of the causal chain. [ABSTRACT FROM AUTHOR]

Congestion is an economic phenomenon of the production process in which the excessive values of inputs lead to a reduction of the outputs. As the existence of congestion makes to increase costs and decreases efficiency, this issue is not acceptable for decision makers. Hence, many methods have been proposed to detect the congestion in the Data Envelopment Analysis framework (DEA). Most of these methods are designed to deal with the decision making units (DMUs) that have no network structure. However, in most real-world applications, some units are composed of independent production subunits. Therefore, a new scheme is required to determine the congestion of such units. A multi-function parallel system is a more common case in the real world that is composed of the same number of subunits such that each subunit has specific functions. In this paper, considering the operation of individual components of each DMU, a new DEA model is proposed to identify and evaluate the congestion of the multi-function parallel systems. It is shown that the proposed method is highly economical in comparison with the existing black-box view from a computational viewpoint. Then, the proposed model is illustrated using a numerical example along with a real case study. [ABSTRACT FROM AUTHOR]

Background: The outbreak of Coronavirus in late 2019 and its continuation in the following years has affected all human societies, government organizations, and health systems. Access to health services is an important issue during crises. The present study aimed to investigate the effect of the Covid-19 pandemic on the consumption of health services in the public sector compared to the private sector in Iran. Methods: The research population consisted of all insured individuals covered by Iran Health Insurance Organization in Fars province, which amounts to approximately 2,700,000 people. The required information including the utilization of laboratory, radiology, medicine, and hospitalization services was extracted on a monthly basis from February 2019 to February 2021. The Multiple Group Interrupted Time Series Analysis (MGITSA) was used for data analysis along with STATA.15 software. Results: According to the findings of MGITSA, in the short-term, the utilization of private laboratory, radiology, medication, and hospital admissions had decreased by approximately 18,066, 8210, 135,445, and 1086 times, respectively (P < 0.05). In the long-run, the use of laboratory and radiology services had increased by about 2312 and 514 times (P < 0.05), respectively. The comparison between the public and private sectors showed that in the short-term, the use of radiology services decreased by about 12,525, while the use of medication increased by about 91,471 times (P < 0.05). In the long-run, the use of laboratory services decreased by about 1514 times (P = 0.076) and no change was observed in the other services utilization (in public relative to private centers). Conclusions: Utilization of health services in the public versus private centers, except for medication and hospitalization, significantly decreased in the short-term. However the utilization of most services returned to the usual trend in the long-term. The reduction in access to health services could impose a significant burden of various diseases, at least in the short-term, and increase health costs in the coming years. [ABSTRACT FROM AUTHOR]

Background: Reinfection with Coronavirus Diseases 2019 (COVID-19) has raised remarkable public health concerns globally. Therefore, the present retrospective cohort study intended to investigate COVID-19 reinfection in registered patients of Fars province in Iran from February 2020 to April 2021. Methods: The patients' data, including the COVID-19 infection, symptoms, comorbidities, and demographics, were collected using the Health Information Systems (HISs). The patients were divided into three groups in terms of the duration between the initial infection and reinfection, including 28-44, 45-89, and more than 90 days. Following the univariate analysis, logistic regression was used to investigate the factors effective on COVID-19 reinfection. Results: A total of 213768 patients had a positive Polymerase Chain Reaction (PCR) test. The reinfection rate was 0.97% (2079 patients). Of these re-infected individuals, 14.9%, 18.5%, and 66.6% had their second positive test 28-45, 45-89, and ≥90 days later, respectively. The mean duration between the initial infection and reinfection was 130.56 days (29-370 days). The chance of reinfection was significantly higher in the youths (Odds Ratio (OR)=2.055; P<0.001), men (OR=1.283; P<0.001), urban population (OR=1.313; P<0.001), and healthcare providers (OR=4.453; P<0.001). The patients with chronic pulmonary diseases, chronic kidney diseases, and malignancy were 1.421 (P=0.036), 2.239 (P<0.001), and 3.437 (P<0.001) times, respectively, more likely prone to reinfection. Conclusion: The results of this study showed that there is a higher risk of reinfection in several vulnerable groups including healthcare providers, young individuals, residents of urban areas, men, and individuals with underlying diseases. [ABSTRACT FROM AUTHOR]

Purpose. The purpose of this study is to sensitivity analysis analyze the returns to scale in two-stage network based on DEA models. The main focus of the firms has always been to obtain the maximum output with the least available resources, which points to the improvement of the firm's performance and the importance of returns to scale and technical improvement. Design/Methodology/Approach. This study examines the sensitivity of returns to scale classifications in a two-stage DEA network. A new input-oriented model was progressed to identify the efficient decision-making units in the two-stage network, after which a new method of determining the returns to scale classifications in the efficient DMUs in two-stage network (constant, increasing, or decreasing returns to scale) was established. Findings. The stability of the returns to scale classifications in the two-stage network was analyzed. A stability region for changes in primary inputs and final outputs is only determined especially for DMUs that are efficient so that it maintains the classification of the returns to scale units. The results are shown by numerical examples. Practical Implications. The sensitivity analysis of returns to scale classifications is one of the most significant issues in data envelopment analysis (DEA), which plays an essential role in management decisions. Originality/Value. Using this model can help improve the performance of companies by using new tools and also improve the quality of work and increase acceptance competition. [ABSTRACT FROM AUTHOR]

In this paper, a novel artificial intelligence technique for the estimation of near-optimal resource management is proposed. The model utilizes a two-stage data envelopment analysis to find the best-practice frontier of the decision-making units. By employing this data, a supervised multi-layer Artificial Neural Network is exercised. This network is capable of predicting the frontier for the near future by receiving input and mediator variables. In the next step, a genetic algorithm is formed to find an optimal input value for the artificial neural network, such that the overall performance of decision-making units in the near future is maximized. The proposed algorithm allows the managers to set some restrictions on the whole system, including the minimum efficiency and the maximum change on resources. The performance of the presented technique is reviewed on 31 branches of an insurance company, during the years 2015 to 2018. The results show that the developed algorithm can efficiently maximize the overall performance of decision-making units. [ABSTRACT FROM AUTHOR]

Resource allocation and target setting is part of the strategic management process of an organization. In this paper, we address the important issue of "optimally" allocating additional resources to the different operating units. Three different managerial interpretations of this question are presented, differing on the assumptions on the expected output increases. In each case, using multiplier data envelopment analysis (DEA) models and common set of weights (CSW), a new procedure for resource allocation and target setting is proposed. The proposed approach is innovative in its use of CSW and multi objective optimization, both of which are consistent with the centralized decision making character of the problem. The validity and usefulness of the proposed CSW–DEA models is shown using different datasets. [ABSTRACT FROM AUTHOR]

One of the challenges for suppliers is to increase their market share due to the limited target market. In other words, in the supply chain, the demand for suppliers' products is limited. Therefore, suppliers produce a determined share of the required amount by producers. However, when it comes to the share in the amount of output among suppliers, the output of suppliers in this indicator is interdependent. In the classical data envelopment analysis (DEA) models, there are no models that assess the suppliers according to the dependence of at least one output on each other. In this paper, a model is presented that can assess sustainable suppliers in the presence of interdependent output among suppliers by using DEA models and their extension. It can also determine the total benchmarks of decision making units (DMUs) in such a way that satisfies the interdependent output constraint. In other words, benchmarks are not determined independently. Ultimately, an approach is presented to determine efficient projections for inefficient DMUs by considering the concept of interdependent output. To represent the applicability of our proposed model, a dataset for two consecutive years including 32 sustainable suppliers with consideration of interdependent output has been implemented using the model presented in this paper. The resulting sustainability has been compared with a classical model. [ABSTRACT FROM AUTHOR]

Objective: This study was conducted to evaluate the effect of the Iran's Health Transformation Plan on the frequency of natural vaginal deliveries (NVDs), cesarean sections (CSs), and total deliveries in the Fars province of Iran. Results: Average number of total deliveries before and after the reform were 3946 and 3810, respectively (p = 0.164). The ratio of CS to total deliveries in the first study month was 54%. This rate reached 47% in the last month (p < 0.01). However, it had much fluctuation trend. The ITSA results showed that in the short-run, the NVD rate increased (β = 492.79, p < 0.01), the rate of CS decreased (β = − 407.09, p < 0.01), and total deliveries increased (β = 85.75, p < 0.724). However, in the long-run, the NVD (β = 5.74, p < 0.423), CS (β = 10.21, p < 0.189), and total deliveries (β = 15.96, p < 0.256) had no significant changes after the reform. Encouraging the NVD package was influential in the short-run but not in the longrun in Iran. Pricing and supply-side policies could not reduce the rate of non-clinical CS on their own. Therefore, paying attention to demand-side policies and changes in consumer behaviors, such as educating the women at the age of pregnancy about the advantages and disadvantages of CS and NVD and correcting misconceptions, could be helpful. [ABSTRACT FROM AUTHOR]

The assessment of efficiency is always of particular importance according to different indicators from different perspectives. There are various techniques for evaluating petrochemical companies, among which the data envelopment analysis technique is one of the best techniques that can be used to calculate the relative efficiency of a set of decision‐making units with network structures. In the present paper, seven petrochemical companies listed in the Iranian stock exchange were analysed. These companies were evaluated in terms of financial performance and sustainable development, and their relative efficiency was calculated during 2015–2016. According to the obtained results, only Marun Petrochemical Co. was found to be efficient in all areas and years. The results also showed that four companies were efficient in financial terms over the period under study. In the general conclusion regarding the companies' performance, Marun was ranked first, Jam was ranked second, and Zagros was ranked third. [ABSTRACT FROM AUTHOR]

Objective: Physicians as an economic firm make use of available resources such as time, human forces and space to provide healthcare services. The current study aimed at estimating the technical efficiency of Iranian self-employed general practitioners (GPs) and its effective factors using data envelopment analysis and regression analysis. Results: About 2% of the GPs were fully efficient and the remaining (98%) were inefficient. Almost, 2.09% of the physicians had constant returns to scale, and 31.41% and 66.49% of them had increasing and decreasing returns to scale, respectively. According to the regression estimates, gender (female) (β = 3.776, P = 0.072), age (β = 0.475, P = 0.013), practice experience (β = − 0.477, P = 0.015), contract with the insurer (β = − 6.475, P = 0.005) and economic expectations (β = 1.939, P = 0.014) showed significant effect on GPs inefficiency. Most of the GPs surveyed did not optimally allocate their time and physical and human resources to provide their services. Female GPs, older ones, those with fewer practice experience, those with higher economic expectations, and the GPs with no insurance contract were more inefficient. Increasing the insurance coverage of self-employed GPs and providing them with training in office economic management can reduce their inefficiency. [ABSTRACT FROM AUTHOR]

Data envelopment analysis technique, not only evaluates Decision making units (DMUs), but also introduces a benchmark for inefficient DMUs. By having the data related to the benchmark and using it appropriately, the DMU under the assessment determines how to eliminate its inefficiency and become efficient. The closer the evaluated DMU is to the presented target, the faster and easier it reaches the efficiency frontier. Furthermore, target setting in the presence of weight restrictions is an important issue. In this paper, a model for finding the closest target, in the presence of weight restrictions is presented. Thus, taking into account trade-offs, as well as weight restrictions, the closest target for each DMU is introduced. The proposed model is also compared with the previous models. Finally, administrating the proposed model for evaluating one of Iranian Banks, the least changes to inefficient branches is represented. [ABSTRACT FROM AUTHOR]

Background: Hemophilia is known as one of the most common coagulation disorders whose treatment costs are particularly high in developing countries, and about 90% of them are related to factor VIII (FVIII) and direct medical costs (DMCs). Thus, the present study aimed to analyze cost-utility of two FVIII diet therapies prepared using blood plasma and recombinant technique. Methods: This study was an economic evaluation fulfilled through a cost-utility approach. To this end, a total number of 120 patients were randomly selected using Krejcie & Morgan's Table and then received blood plasma and recombinant FVIII. The decision tree structure was also utilized to estimate economic and clinical outcomes. Moreover, costs were reviewed from societal perspective. Quality-adjusted life year (QALY) was subsequently determined as the measure of effectiveness (MOE). Besides, one-way (univariate) sensitivity analysis was performed to quantify uncertainty effects of the study parameters. The information was ultimately analyzed using the TreeAge Pro 2011 and the Microsoft Office Excel 2010 software. Results: The results revealed that the recombinant diet therapy had higher costs and effectiveness compared with blood-plasma-derived FVIII, so that the mean costs of these two diet therapies were equal to 37,624 and 20,349 purchasing power parity (PPP) $ with utility scores of 0.78 and 0.62; respectively. Since the incremental cost-effectiveness ratio (ICER) for the recombinant medications was over three times of the threshold level, it was considered as overwhelming because of its high cost in spite of its better effectiveness. Moreover, the results of one-way (univariate) sensitivity analysis demonstrated the highest sensitivity to the utility in patients who had been injected with blood-plasma-derived FVIII and had been successfully treated. Conclusion: The study results revealed that FVIII prepared using blood plasma for hemophilia A patients had higher cost-effectiveness compared with that made using recombinant technique. [ABSTRACT FROM AUTHOR]

Background: Preventing violence is important especially in the Middle East, where many countries are struggling with violence. Knowing the affecting factors could help public policy makers to decrease violence level. Thus, this study is aimed to analyze health and other socio-economic factors that could affect interpersonal violence in middle eastern countries. Methods: From international organization databases, we collected the panel data of Middle Eastern countries from 1990 to 2016 on prevalence of interpersonal violence as dependent variable and per capita income, life expectancy, democracy index (DI), urbanization and unemployment as explanatory factors. Several panel data diagnostic tests were performed for selecting a suitable model of estimation. The variables were entered in the model in logarithmic form. Because of heteroscedasticity, cross-sectional dependence and serial correlation of residuals, feasible generalized least squares (FGLS) was used for estimation of mentioned model using Stata 14.2. Results: The means of interpersonal violence prevalence and life expectancy were 2462.2 (SD = 232.4) per 100 000 population and 73.5 (SD = 4.5) in the Middle East, respectively. Urbanization (β = -0.0925, P < 0.01), life expectancy (β = -0.0362, P < 0.01), per capita income (β = -0.0046, P < 0.01), unemployment (β = 0.0007, P < 0.01) and democracy (β = -5.83e-06, P < 0.01) had significant relation with interpersonal violence. Conclusion: Life expectancy as a proxy for health is one of the main predictors of interpersonal violence, as literature supports. That is, if a society is healthier, the burden of interpersonal violence will be lower. Thus, health policy makers should consider health status as a preventive factor of violence, which is stated in health as a bridge for peace by the world health organization. [ABSTRACT FROM AUTHOR]

Cap-and-trade is regarded as the most effective approach to control and reduce greenhouse gas emission. How to perform the reallocation in a fair way is very critical to control total amount of emissions and improve trade mechanism. It has been proved that data envelopment analysis (DEA) is an effective way for reallocation. The objective of the present paper is to develop a centralized DEA model to reallocate emission permits in the cap and trade system based on countries efficiencies. Presented model considers all decision making units (DMUs) together and improves whole efficiency of them by reducing total emission permit as undesirable outputs. Also, this model determines amount of emitted gases that can be reduced without reducing other outputs. To demonstrate the applicability of model, a case study is presented. Sensitivity analysis is carried out to investigate the impact of the some parameters on the results. [ABSTRACT FROM AUTHOR]

The initial issue that must be addressed in teamwork is the manner in which decisions are made by the group and its members. Voting is a procedure to aggregate individual votes to achieve a collective decision. Since individuals have varied opinions and preferences, preferential voting assists in conveying the priorities of the voters to the society or community. In many circumstances, such as voting-based managerial decisions, voters are of a voting preference of unequal voting power. This paper presents a method for the ranking of preferential voting with voters of unequal voting power, which, in addition to the utilization of preference voting models, employs the DEA and assurance region techniques. The deployment of DEA technique causes an increment in the competence of discriminating the ranking of candidates, and in the finale the proposed method is expressed for an empirical example to rank the petrochemical companies in the Tehran Stock Exchange. [ABSTRACT FROM AUTHOR]

Abstract Regarding the importance of budgeting in organizations, this research proposes an empirical approach to budget allocation problems. The methodological instrument utilized is data envelopment analysis (DEA) which is a nonparametric mathematical programming technique. In the DEA methodology a standard DEA model should be independently solved to evaluate each decision making unit (DMU). Consequently, it is hard to find the magnitude of budget for each DMU by applying a budget allocation model based on standard DEA models because identifying the DMU under evaluation is problematic. Also, to overcome problems of evaluation using standard DEA models, common set of weights (CSW) DEA models were suggested. These models can be developed for use in budget allocation DEA models that lead to finding a single magnitude of budget for each DMU. Moreover, the opinion of the decision maker can be incorporated into the model using budgetary constraints. As a result, a restricted linear budget allocation CSW DEA model is proposed in which the central authority would like to plan for improving the total efficiency scores of all DMUs. In essence, the proposed model is used to reallocate the available budget and, thus, the results obtained will be a suggestion for budget allocation in subsequent periods. Finally, the proposed model is applied to budget allocation in the Iranian gas industry in which the available budget is reallocated to increase the total efficiency scores of Iranian gas distribution branches. Highlights • This research proposes an empirical approach to budget allocation problems. • The proposed model for use in budget allocation is a CSW DEA model. • The proposed model is based on MCDM; thus, it has a robust background. • Indeed, the proposed model is used to reallocate the available budget. • The proposed model is applied to the optimizing budget allocation in Iranian gas industry. [ABSTRACT FROM AUTHOR]

In most production activities, desirable and undesirable outputs are produced together, and they are interdependent. As a result, reducing undesirable outputs can also decrease desirable ones. This study introduces concepts and models and applies them to a practical example for the first time, considering the dependency of production between desirable and undesirable outputs in an SBM network system. The aim is to assess the sustainability of supply chains. To achieve this goal, a new network data envelopment analysis (NDEA) model is developed based on the Slack Based Model (SBM). A case study is presented to evaluate the sustainability of wheat supply chains, which demonstrates various aspects of the proposed model, which considers both desirable and undesirable outputs produced together. • A new SBM network model is introduced to handle the jointly produced unwanted (bad) outputs and desirable outputs. • The possible changes of desirable and unwanted outputs are combined into the NDEA model. • Managerial strategies for altering the desirable and unwanted (bad) outputs are provided. [ABSTRACT FROM AUTHOR]

Background: The labor supply of nurses, as one of the main healthcare workers, is an important issue in health human resources planning in all health systems. Finding the factors affecting it, could help policymakers to solve the shortage of nursing work supply. The present study aimed to investigating the quantity and factors affecting the nurses' labor supply in Iran. Method: In this cross-sectional study, a sample of 598 nurses working in public hospitals of Shiraz (Iran) were selected via proportionate stratified random sampling method. The required data was collected using a structured questionnaire which asked working hours and other related factors. To analyze the data, descriptive statistics, univariate analysis and multivariate linear regression were performed using STATA 15. The multivariate labor supply model was estimated separately for married and single nurses. Results: The average weekly working hours of nurses was 54.65 h in all medical centers and 50.28 h in the main hospital. The regression results showed that the labor supply of nurses with work experience (β = − 0.368, P = 0.014), satisfaction with work shift arrangement (β = − 2.473, P = 0.001), income between 60-89 million rial (β = − 14.046, P = 0.002), income between 90-119 million rial(β = − 12.073, P = 0.012), and working in the emergency department (β = − 5.043, P = 0.017) had negative and significant relationship; But there was a positive and significant relationship with satisfaction of the work environment (β = 1.86, P = 0.011), workload at work (β = 1.951, P = 0.023) and employment status (contractual employees) (β = 4.704, P = 0.004). Conclusion: The labor supply function of nurses is affected by demographic, economic and non-economic factors. The most contributing factors were related to non-economic variables. It seems that the non-financial cost and benefits related to the job as well as internal factors have more important role on the nurses' labor supply. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to present a new approach for measuring the performance of n-stage series systems in the presence of uncertain data, which are two challenging issues in evaluating the efficiency of Decision-Making Units (DMUs) using traditional Data Envelopment Analysis (DEA) models. By using a Network DEA model and its dual, as well as using Bertsimas et al.'s robustness technique, two Robust Network DEA have been presented. These models can display a range of DMUs performance with appropriate accuracy. Proposed models were used to determine the efficiency range of Iranian dairy companies' supply chain with three stages. The results show that the proposed models are applicable and effective. Total efficiency bounds are obtained with percentage deviations of 20%, 10% and 5%. The lower bounds have relative errors of 0.39, 0.23 and 0.12 and a correlation coefficient of more than 97%, and the upper bounds have relative error of 1.1, 0.84 and 0.62 and a correlation coefficient of about 90%. Therefore, the proposed model for calculating the lower bound is more accurate. The calculation of the efficiency bounds of the sub-stages also confirms this issue. Finally, the obtained results have been compared with the values obtained through a fuzzy three-stage DEA model, our results have a higher correlation coefficient and more accurate upper bounds. [ABSTRACT FROM AUTHOR]

Background: Diabetes is one of the most common chronic and costly diseases worldwide and type 2 diabetes is the most common type which accounts for about 90% of cases with diabetes. New medication-therapy regimens such as those containing linagliptin alone or in combination with other medications (within the category of DDP-4 inhibitors) must be evaluated in terms of efficacy and compared with other currently used drugs and then enter the medication list of the country. Hence, this study aimed to compare the clinical efficacy of the two drugs, i.e. linagliptin and sitagliptin, in patients with type 2 diabetes. Methods: A systematic review was conducted to identify all clinical trials published by 2015 which compared the two drugs in patients with type 2 diabetes. Using keywords such as "linagliptin", "type 2 diabetes mellitus", "sitagliptin" and related combinations, we searched databases including Scopus, PubMed, and Web of Science. The quality of the selected studies was evaluated using the Jadad score. Considering primary and secondary outcomes extracted from the reviewed studies, a network meta-analysis was used to conduct a systematic comparison between the two studied drugs. Results: This network meta-analysis included 32 studies (Linagliptin vs PLB: n = 8, Sitagliptin vs PLB: n = 13, Linagliptin + MET vs PLB + MET: n = 4, and Sitagliptin + MET vs PLB + MET: n = 7) and a total of 13,747 patients. The results showed no significant difference between linagliptin and sitagliptin in terms of key efficacy and safety outcomes such as HbA1c changes from baseline, body weight change from baseline, percentage of patients achieving HbA1c <7, and percentage of patients experiencing hypoglycemic events (p > 0.05). The results showed that the efficacy of the two drug regimens was the same. Conclusions: Based on the results, there was no significant difference between the two drugs, i.e. linagliptin and sitagliptin, in terms of efficacy; in other words, the efficacy of the two drugs was the same. Therefore, the use of these two drugs depends on their availability and cost. [ABSTRACT FROM AUTHOR]

As an important concept in data envelopment analysis (DEA), elasticity measure has wide theoretical and practical applications in formulating various economic concepts. Anchor points also appear to be particularly interesting and highly useful in DEA, especially for recognizing a decision making unit (DMU) as a benchmark. This paper is an attempt to use left-and right-hand elasticity measures to present a novel definition (characterization) for anchor points. The study results reveal that if there exists an increase in a bundle of input with no rate of change in a bundle of output or if there is a decrease in a bundle of output, but a bundle of input has no rate of change, then such an extreme point is the anchor point. [ABSTRACT FROM AUTHOR]

When there is an extensive number of inputs and outputs compared to the number of DMUs, one of the drawbacks of Data Envelopment Analysis appears, which incorrectly classifies inefficient DMUs, as efficient ones. Accordingly, the DEA ranking power becomes further moderated. To improve the ranking power, this paper renders the details of an algorithm that presents a model combining the Principal Component Analysis and the Slacks-Based Measure (PCA-SBM) which reduces the number of the incorrectly determined efficient DMUs. Also to complete ranking of DMUs, the algorithm presents a Super-Efficiency model integrated with PCA (PCA-Super SBM) which can rank the efficient DMUs (extreme and non-extreme). Whereas the most important previous models for ranking efficient units cannot rank non-extreme ones. Additionally, in most previous studies, DEA models combined with PCA fail to handle negative data, while, the presented models can cover this data. Two case studies (pharmaceutical companies listed on the Iranian stock market and bank branches) are manipulated to demonstrate the applicability and performance of the algorithm. To show the superiority of the presented models, the SBM model without PCA and the Super SBM model without PCA have been implemented on the data of both cases. In comparing the two methods (PCA-SBM and SBM), the PCA-SBM model has higher ranking power (five efficient DMUs versus nineteen in the case of pharmaceutical companies and four efficient DMUs versus twenty-nine in the case of bank branches). Also in comparing the PCA-Super SBM and Super SBM, the PCA-Super SBM model works more powerfully in complete ranking. As the Super SBM model cannot rank non-extreme units unlike the PCA-Super SBM. Consequently, the presented algorithm works successfully in ranking the DMUs completely (inefficient, extreme, and non-extreme efficient) with low complexity. [ABSTRACT FROM AUTHOR]

One of the most complicated decision making problems for managers is the evaluation of supply chain (SC) performance which involves various criteria. Though vast studies have been recorded on supply chain efficiency evaluation via balanced scorecard (BSC) approach, these studies do not focus on the relationships between the four perspectives of BSC approach. The present paper is an attempt focusing on these relationships, especially the returnable ones. To do so, at first, all relationships between the four perspectives of BSC were determined and then the DEMATEL approach was employed to obtain a network structure. This network structure was then used to create a network DEA model. Since it was not possible to calculate the efficiency evaluation score by BSC, the data envelopment analysis (DEA) model was used for such an evaluation. Moreover, after reviewing different tools to evaluate the performance of supply chain, a new approach, relying on network DEA with BSC approach, was generated. Finally, this model was applied in the Iranian food industry to evaluate its supply chains efficiency and the results proved the high efficiency of the model designed. The findings could be used in various evaluation processes in different industries. [ABSTRACT FROM AUTHOR]

Congestion is an economic phenomenon in the production process where excessive amounts of the input cause a reduction of the output. The motivation of this paper is to investigate and compare the methods provided for estimating congestion in the DEA literature. [ABSTRACT FROM AUTHOR]

Abstract: The vegetable oil industry is considered as one of the major industries contributing to energy consumption in Iran. Despite the increasing need to improve energy efficiency in this industry, previous studies show that energy-efficient technologies have not been applied in most plants and other energy efficiency measures have not yet been adopted. Accordingly, this paper addresses the implementation of energy efficiency measures in the vegetable oil industry. Using a data envelopment analysis (DEA), the relative efficiency of plants in the vegetable oil industry is compared at different stages of the implementation of energy efficiency measures. The most effective indices for improving energy efficiency were identified and a model was designed to implement energy efficiency measures in industrial plants. In the model, the implementation process consisted of 5 stages, each of which had its own inputs and outputs. A multi-stage DEA model was employed to evaluate the performance of vegetable oil plants at each individual stage as well as the aggregate efficiency of each plant. After estimating the total efficiency of all plants and evaluating performance at the individual stages for each plant, the plants with ideal conditions for implementing energy efficiency measures were identified. These plants can serve as a benchmark for other plants. Estimation of the average efficiency across plants at each stage indicated that the plants suffered from poor performance in the first and fourth stages. Therefore, the results of this study indicate a need to develop new strategies for improving energy efficiency at these two stages. [Copyright &y& Elsevier]

In this paper, while data envelopment analysis (DEA) technique has been used, four-stage Malmquist Productivity Index has been mooted in order to investigate the evaluation of balanced scorecards in commercial banks. With this index the progress and regress of each sub-process, exists within each decision-making units, can be examined. Finally, with an application in commercial banks validity of the proposed models has been demonstrated. [ABSTRACT FROM AUTHOR]

It is well known that data envelopment analysis (DEA) models are sensitive to selection of input and output variables. As the number of variables increases, the ability to discriminate between the decision making units (DMUs) decreases. Thus, to preserve the discriminatory power of a DEA model, the number of inputs and outputs should be kept at a reasonable level. There are many cases in which an interval scale output in the sample is derived from the subtraction of nonnegative linear combination of ratio scale outputs and nonnegative linear combination of ratio scale inputs. There are also cases in which an interval scale input is derived from the subtraction of nonnegative linear combination of ratio scale inputs and nonnegative linear combination of ratio scale outputs. Lee and Choi (2010) called such interval scale output and input a cross redundancy. They proved that the addition or deletion of a cross-redundant output variable does not affect the efficiency estimates yielded by the CCR or BCC models. In this paper, we present an extension of cross redundancy of interval scale outputs and inputs in DEA models. We prove that the addition or deletion of a cross-redundant output and input variable does not affect the efficiency estimates yielded by the CCR or BCC models. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) models can calculate the Malmquist Productivity Index (MPI). Classic Malmquist Productivity Index shows regress and progress of a DMU in different periods with efficiency and technology variations without considering the present value of money. This issue is of major importance since while a currency of in previous year is not equal to that of now this would yield bias results which can affect the correct interpretation. The index developed here is defined in terms of Modified Malmquist Productivity Index model, which can calculate progress and regress by using the factor of present time value of money. The incorporation of present time value of money is also calculated within the framework of data envelopment analysis. This factor is fundamental and should be considered in DEA Malmquist Productivity Index. Moreover, here, differences between presented models are compared to those of previous ones indeed, biased results will be shown in the case study in banks, and problem and solution have been investigated in the literature. [ABSTRACT FROM AUTHOR]

Abstract: Scoring rules are an important disputable subject in data envelopment analysis (DEA). Various organizations use voting systems whose main object is to rank alternatives. In these methods, the ranks of alternatives are obtained by their associated weights. The method for determining the ranks of alternatives by their weights is an important issue. This problem has been the subject at hand of some authors. We suggest a three-stage method for the ranking of alternatives. In the first stage, the rank position of each alternative is computed based on the best and worst weights in the optimistic and pessimistic cases, respectively. The vector of weights obtained in the first stage is not a singleton. Hence, to deal with this problem, a secondary goal is used in the second stage. In the third stage of our method, the ranks of the alternatives approach the optimistic or pessimistic case. It is mentionable that the model proposed in the third stage is a multi-criteria decision making (MCDM) model and there are several methods for solving it; we use the weighted sum method in this paper. The model is solved by mixed integer programming. Also, we obtain an interval for the rank of each alternative. We present two models on the basis of the average of ranks in the optimistic and pessimistic cases. The aim of these models is to compute the rank by common weights. [Copyright &y& Elsevier]

Abstract: In data envelopment analysis (DEA), efficient decision making units (DMUs) are of primary importance as they define the efficient frontier. The current paper develops a new sensitivity analysis approach for a category DMUs and finds the stability radius for all efficient DMUs. By means of combining some classic DEA models and with the condition that the efficiency scores of efficient DMUs remain unchanged, we are able to determine what perturbations of the data can be tolerated before efficient DMUs become inefficient. Our approach generalizes the conventional sensitivity analysis approach in which the inputs of efficient DMUs increase and their outputs decrease, while the inputs of inefficient DMUs decrease and their outputs increase. We find the maximum quantity of perturbations of data so that all first level efficient DMUs remain at the same level. [Copyright &y& Elsevier]

Abstract: Data envelopment analysis (DEA) and multiple objective linear programming (MOLP) can be used as tools in management control and planning. The existing models have been established during the investigation of the relations between the output-oriented dual DEA model and the minimax reference point formulations, namely the super-ideal point model, the ideal point model and the shortest distance model. Through these models, the decision makers’ preferences are considered by interactive trade-off analysis procedures in multiple objective linear programming. These models only consider the output-oriented dual DEA model, which is a radial model that focuses more on output increase. In this paper, we improve those models to obtain models that address both inputs and outputs. Our main aim is to decrease total input consumption and increase total output production which results in solving one mathematical programming model instead of n models. Numerical illustration is provided to show some advantages of our method over the previous methods. [ABSTRACT FROM AUTHOR]

Abstract: In a multi-objective linear fractional programming problem (MOLFPP), it is often useful to check the efficiency of a given feasible solution, and if the solution is efficient, it is useful to check strong or weak efficiency. In this paper, by applying a geometrical interpretation, a linear programming approach is achieved to test weak efficiency. Also, in order to test strong efficiency for a given weakly efficient point, a linear programming approach is constructed. [Copyright &y& Elsevier]

Finding the appropriate weight for each criterion is one of the main points in Multi Attribute Decision Making (MADM) problems. Shannon's entropy method is one of the various methods for finding weights discussed in the literature. However, in many real life problems, the data for the decision making processes cannot be measured precisely and there may be some other types of data, for instance, interval data and fuzzy data. The goal of this paper is the extension of the Shannon entropy method for the imprecise data, especially interval and fuzzy data cases. [ABSTRACT FROM AUTHOR]

Background: Outsourcing as an efficiency-oriented approach was introduced to elevate productivity in the public sector. This strategy as a public-private partnership (PPP) arrangement was coined to increase efficiency and quality in public organizations. Objectives: In this study, we aimed to investigate the quantity of applying common outsourcing models at Bushehr University of Medical Sciences (BUMS). Methods: This descriptive and cross-sectional study was conducted in 2020 at BUMS. The research populationwas allBUMSdeputies, hospitals, and health networks. This study was carried out in three stages, including a literature review, the construction of a checklist and finally the quality assurance of gathered data. Descriptive indices were calculated using excel software version 2016. Results: Results showed a total of seven various outsourcing models on which 189 cases of services had been outsourced. Of the 189 cases, 108 were clinical, and the rest were non-clinical. Among the outsourcing models, the service purchasing model was the most typically used, and leases and divestitures were the two least used models. Conclusions: Although various outsourcing models are available, they have been disproportionately utilized, showing that to spread thedomainof penetration of this strategy, we need to tackle the obstacles ahead of its successful implementation. Increasing the knowledge of managers could be an effective solution. Furthermore, capacity-building toward raising the number of private healthcare providers could facilitate the implementation of an outsourcing policy. [ABSTRACT FROM AUTHOR]

An effective method for evaluating the efficiency of peer decision-making units (DMUs) is data envelope analysis (DEA). In engineering sciences and real-world management problems, uncertainty in input and output data always exists. To achieve reliable results, uncertainties must be taken into account. In this research, a General Fuzzy (GF) approach is designed to cope with uncertainty in the presence of fuzzy observations for categorizing and specifying stability radius and alterations ranges of efficient and inefficient DMUs, which is applicable to real-world decision-making problems. For this purpose, a DEA sensitivity analysis model is presented, which will be modeled by fuzzy sets. Then, by applying the General Fuzzy (GF) approach, the fuzzy DEA sensitivity analysis model is transformed into the equivalent crisp form of fuzzy chance constraints according to specific confidence levels. Finally, a numerical example and a case study of branches of the social security organization are presented to illustrate sensitivity and stability analysis in the presence of fuzzy data. The obtained results provide the input and output changes of the evaluated units according to the attitude and preference of the decision maker with different confidence levels so that the data changes in the fuzzy environment do not change the units' classification from efficient to inefficient and vice versa. [ABSTRACT FROM AUTHOR]

One of the challenging and important subjects in Data Envelopment Analysis (DEA) is the ranking of Decision Making Units (DMUs). In this paper, a new method for ranking the efficient DMUs is firstly proposed by utilizing the DEA technique and also developing a capable metaheuristic, imperialist competitive algorithm, derived from social, political, and cultural phenomena. Efficient DMUs are known as colonizers, and the virtual units, which are within their regions of exclusive domination, are considered as colonies. Efficient units are ranked by utilizing the factor of competition among imperialists to attract each other's colonies. One advantage of proposed method is that, without solving any mathematical, and complex solution approaches, all extreme and non-extreme units are ranked only by comparing the pairs. [ABSTRACT FROM AUTHOR]

Background: Inappropriate prescriptions can lead to adverse consequences for patients. It also imposes excessive cost on the patients, payers and health systems. The current study aimed at estimating the rate of inappropriate brain Magnetic Resonance Imaging (MRI) prescriptions and their financial burden in Iran. Methods: Using systematic stratified sampling method, this cross-sectional study recruited 385 participants from three public teaching hospitals in Shiraz, Iran. Demographic information, questions related to brain MRI prescription and its indications checklist were collected using study-specific data collection tools. The completed indications checklist was compared to the appropriateness status table of indications and scenarios to detect the percent of the appropriateness of prescriptions. Results: About 21 percentage of total brain MRI prescriptions are inappropriate. Previous treatment, number of referrals to physician, having other diagnostic tests and the applicant of MRI (P < 0.01) had significant relationships with prescription appropriateness. The estimated financial burden of inappropriate brain MRIs in Shiraz teaching hospitals was 99,988 US dollar in 2017. Conclusions: More than one-fifth of brains MRIs were inappropriate (i.e. prescriptions without medical indications). It caused 99,988 United States Dollar (USD) financial burden which is 17 times that of Iran's Gross Domestic Product (GDP) per capita. To better allocate resources for the provision of MRI services to health system, rationing policies for controlling moral hazard and reducing provider induced demand can be helpful. [ABSTRACT FROM AUTHOR]

Background: Family physician program (FPP) and health transformation plan (HTP) are two major reforms that have been implemented in Iran's health system in recent. The present study was aimed at evaluating the impact of these two reforms on the level of service utilization and cost of health care services.Methods: This longitudinal study was conducted on people insured by social security organization in Fars province during 2009-2016. The data on the utilization of services and costs of general practitioner visits, specialist visits, medications, imaging, laboratory tests, and hospitalization were collected. Interrupted time series analysis was used to analyze the effect of the two mentioned reforms.Results: FPP resulted in a significant reduction in the number of specialist visits, imaging, and laboratory tests in the short term, and in the number of radiology services, laboratory tests, and hospitalization in the long term. In contrast, HTP significantly increased the utilization of radiology services and laboratory tests both in the short term and long term. Concerning the costs, FPP resulted in a reduction in costs in short and long term except general practitioners' and specialist visit, and medication in long term. However, HTP resulted in an increase in health care costs in both of the studied time periods.Conclusions: FPP has been successful in rationalizing the utilization of services. On the other hand, HTP has improved people's access to services by increasing the utilization; but it has increased health care costs. Therefore, policymakers must adopt an agenda to revise and re-design the plan. [ABSTRACT FROM AUTHOR]

An accurate assessment of performance of Health, Safety, and Environment (HSE) system allows managers to identify strengths and weaknesses in the HSE system. This paper mainly was aimed to assess the performance of HSE management system using Fuzzy Data Envelopment Analysis (FDEA) model in the smelting industry. The indices of performance evaluation of process HSE management systems of smelting industries were weighted and ranked by Fuzzy System. Then, based on the weights of performance indices and data collected in the case study, performance was evaluated via DEA. The FDEA model was solved using a network model with constant returns to scale. According to the results, environment was the most important index of efficiency. Number of HSE Expert (0.16) and annual HSE Budget per employee (0.17) were the most important input indices in HSE performance system. Ergonomic risk control (0.08), Fire source control (0.08), and waste water quality (0.08) were the most important output indices in HSE performance system, respectively. The different performance criteria or safety performance with varying levels of significance can be used in each step of the assessment process. Budget of HSE was ranked the most important of HSE performance input indicators. This index affects other input indicators. Risk control in the three areas of safety, health, and the environment was one of the most important indicators of performance appraisal output. To evaluate systems safety, a performance evaluation system based on multiple inputs and multi-outputs was more applicable than other systems. [ABSTRACT FROM AUTHOR]

Background: Policymakers use simulation-based models to improve system feedback and model the reality of the problems in the system. This study uses the system dynamics approach to provide a model for predicting hospital bed shortages and determine the optimal policy in Shiraz, Southern Iran. Methods: This study was designed based on Sterman's system dynamic modeling (SDM) process. Firstly, we determined the main variables affecting bed distribution using a mixed qualitative and quantitative study which includes scoping review, expert panel, Delphi, and DANP. Then, dynamic hypotheses were designed. Subsequently, we held several expert panels for designing the causal and stock-flow models, formulating and testing a simulation model, as well as developing various scenarios and policies. Results: Dynamic modeling process resulted in four scenarios. All of the scenarios predicted a shortage of national hospital beds over a 20-year time horizon. Then, four policies were developed based on the changes in the number of beds and capacity of home care services; finally, the optimal policy was determined. Conclusions: Due to the high cost of setting up hospital beds, developing and supporting cost-effective home care services, strengthening the insurance coverage of these services, and improving the quantity and quality of community care, considering the real needs of the community could be considered as an optimal option for the future of the city. [ABSTRACT FROM AUTHOR]

Organizations and companies have realized the significance of Health, Safety and Environmental (HSE) management as an integral and vital part of modern factories and organizations. Developing HSE processes requires performance assessment, for which defining appropriate indicators is a necessity. Because of the nondeterministic nature of performance indicators, assessments are prone to influence from personal judgments by the assessors. Many HSE management system indices are uncertain. Fuzzy approaches can reduce the effects of assessor judgments as well as uncertainty. The aim of the study was to present an HSE management system performance assessment model in a fuzzy environment. A questionnaire was used to conduct the study in one of the largest steel companies in Iran. The results revealed that, in the areas of health, safety, and environment, control of disease, fire hazards, and air pollution were of paramount importance, with coefficients of 0.057, 0.062, and 0.054, respectively. Furthermore, health and environment indicators were the most common causes of poor performance. Many performance health indicators had remained unchanged which were also due to the long-term nature of health indicators. Finally, it was shown that HSE management systems could affect the majority of safety indicators the short run, whereas health and environment indicators require longer periods. [ABSTRACT FROM AUTHOR]

Background: Depression disorders are a leading cause of disability in the world which imposes a significant economic burden on patients and societies The present study aimed to determine the economic burden of Major Depressive Disorder (MDD) on the patients referred to the reference psychiatric single-specialty hospitals in southern Iran in 2020. Methods: This cross-sectional research is a partial economic evaluation and a cost-of-illness study conducted in southern Iran in 2020. A total of 563 patients were enrolled through the census method, and a researcher-made data collection form was used to gather the required information. The prevalence-based and the bottom-up approaches were also used to collect the cost information and calculate the costs, respectively. The data on direct medical, direct non-medical, and indirect costs were obtained using the information in the patients' medical records and insurance bills as well as their self-reports or those of their companions. To calculate the indirect costs, the human capital approach was used as well. Results: The results showed that the annual cost of MDD was $ 2717.41 Purchasing Power Parity (PPP) (USD 2026.13) per patient in 2020. Direct medical costs accounted for the largest share of the costs (73.68%), of which hoteling and regular beds expenses were the highest (57.70% of the total direct medical costs). The shares of direct non-medical and indirect costs were 7.52 and 18.80%, respectively, and the economic burden of the disease in the country was estimated at $7,120,456,596 PPP (USD 5,309,088,699). Conclusion: In general, due to the high prevalence of MDD and the chronicity of the disease, the costs of its treatment can impose a heavy economic burden on the society, healthcare system, insurance system, and the patients themselves. Therefore, it is suggested that health policymakers and managers should take appropriate measures to increase the basic and supplemental insurance coverage of these patients. In addition, in order to reduce the costs, proper and equitable distribution of psychiatrists and psychiatric beds, expansion of home care services, and use of Internet-based technologies and the cyberspace to follow up the treatment of these patients are recommended. [ABSTRACT FROM AUTHOR]

In production theory, industrial units do business in such a way that they use minimum amount of resources to produce maximum amount of products. So, inefficient units decrease their inputs level and increase their outputs level to meet the efficient frontier. By changing inputs and outputs, achieving an optimal scale size (OSS) in industrial units is one of the most important attempts and has attracted considerable attention among researchers. In this paper, an optimal scale size in input–output allocative DEA model is defined to each production firm in which the costs of inputs and the revenues of outputs are considered. We first rearrange the average-revenue efficiency measure that combines scale and output allocative efficiencies. Next, we simultaneously consider both of inputs and outputs in a new average-cost/revenue efficiency measure (ACRE). It has been shown that the proposed ACRE measure is the ratio of the profitability efficiency to ray average productivity. A numerical heuristic procedure is proposed to calculate a relatively good approximation of the new OSS in a convex and continuous technology set. To illustrate the real applicability of the proposed approach, we use a real case on 39 electricity distribution companies. [ABSTRACT FROM AUTHOR]

Background: The medical specialty choice of physicians directly affects the healthcare system, community wellbeing, and their own professional life. It is therefore important to identify its underlying factors to predict such medical career choices and to draw up a comprehensive evidence-based public health policy and health human resources planning. The present qualitative study aimed to identify these determinants of career choices through semi-structured interviews with medical doctors in the context of country-specific social and cultural characteristics. Methods: The present qualitative study was carried out in 2019- 2020 at Shiraz University of Medical Sciences (Shiraz, Iran). The target population was medical students, junior doctors, and medical residents. The purposive sampling method was used to recruit the participants and sampling continued until data saturation, i.e., no new information could be extracted. Data were collected through individual, face-to-face, semi-structured interviews and analyzed using the conventional content analysis method. Inductive thematic analysis was used to interpret the data based on which a thematic map was developed illustrating the factors influencing medical specialty preferences in Iran. Data trustworthiness was assessed according to the criteria proposed by Guba and Lincoln. Results: Analysis of the interview data resulted in three main themes and fourteen sub-themes. The main themes were "Personal determinants", "Career-related factors", and "Interpersonal influences". The most important sub-themes were personal interests, income, and prestige. Conclusion: The determinants influencing the specialty preferences of Iranian medical doctors are identified. Our findings will facilitate the development of a comprehensive evidence-based public health policy, health human resources planning, and appropriate medical education policy. [ABSTRACT FROM AUTHOR]

The highest efficiency score 1 (100% efficiency) is regarded as a common benchmark for Decision Making Units (DMUs). This brings about the existence of more than one DMU with the highest score. Such a case normally occurs in all Data Envelopment Analysis (DEA) models and also in all the Common Set of Weights (CSWs) methods and it may lead to the lack of thorough ranking of DMUs. And ideal DMU based on its specific structure is a unit that no unit would do better than. Therefore, it can be utilized as a benchmark for other units. We are going to take advantage of this feature to introduce a linear programming problem that will produce CSWs. The proposed method assures that the efficiency of all the units is less than that of the benchmark unit. As a result, it provides a comprehensive ranking of DMUs. Moreover, the proposed method is also noteworthy regarding computation. A numerical example is suggested to clarify and explain the proposed method and compare it to two other CSWs methods. Finally, 33 universities in Iran were ranked and compared using the proposed method. [ABSTRACT FROM AUTHOR]

Highlights: [•] Traditional Data Envelopment Analysis (DEA) treats bank branches as black boxes. [•] Black box DEA models primarily consider the initial inputs and the final outputs. [•] Intermediate measures are often lost in the traditional black box DEA models. [•] We propose a DEA model for banking with three stages. [•] Two independent parallel stages are linked to a third final stage in the model. [Copyright &y& Elsevier]

Congestion is an essential concept in data envelopment analysis (DEA). It occurs when the decrease in at least one decision-making unit (DMU) input increases at least one output without affecting the other inputs or outputs. Although various methods have discussed congestion determination in static DEA, congestion calculation in dynamic DEA under inter-temporal dependence has not been considered in the literature. This paper deals with the congested input levels in multi-period production systems under inter-temporal dependence and the inter-temporal dependence of production periods due to the stock capital. Due to the importance of evaluating DMUs in the presence of time factors, this paper expands the determination of congestion of DMUs under the time factor and dependence of input and output levels in periods. The proposed models determine the congested unit in the assessment window and characterize the extra-input and lack-output amounts in all of the congested unit components in each period of the assessment window. This paper aims to specify production system performance in different evaluation periods by determining the dynamic congestion of DMUs. This paper proposes a method to identify the congested paths. The main advantage of this method is to prevent inappropriate allocation of the resources during evaluation periods of the assessment window. The validity of the proposed method is investigated through an example in the banking sector. Moreover, this method can be utilized in other sectors to homogeneous units with the same structure. [ABSTRACT FROM AUTHOR]