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Esfordi phosphate mine is the largest phosphate mine in Iran, producing 360 thousand tons of phosphate ore annually. Currently, the mine blasting operation is facing problems such as back break, fines, and flyrock. The main purpose of this study is to propose an approach to achieve an optimal blasting pattern that, while it can produce the desired fragmentation, can also minimize adverse outcomes of blasting. In the first phase of the study, the blasting-related problems in the mine were investigated. Then, the mine was classified into three zones and blasting patterns were designed for each zone. In the second phase, the performance of designed patterns and previously implemented ones were examined using Data Envelopment Analysis (DEA) method. In the next stage, based on questionnaires filled out by experts and using multi-criteria decision making methods, the optimal blasting pattern for each zone of the mine was identified. Implementation of optimal patterns in the mine reduced fine generation by 17 percent and had a significant effect in reducing backbreak and flyrock. This is while there was a drop in drilling and blasting costs too, and a more uniform size of rock fragments was produced. The outcomes of this study indicated that the hybrid analysis of data envelopment analysis and multi-criteria decision making is a successful approach to optimize blasting operation in surface mines, especially where a limited number of input data sets are available. • DEA-MCDM hybrid method is an efficient approach for optimization of blasting. • Applying the proposed design can reduce the fines generation in blasting. •Proposed optimum design increased the uniformity of rock fragment size. • The optimum design reduced drilling and blasting costs and energy consumption. [ABSTRACT FROM AUTHOR]

The continuous growth in energy demand, the inevitable decline in the availability of fossil fuels, and the rising concern about increasing emissions are all precursors to climate change. The aims of this study comprise the assessment of energy flow and greenhouse gas emission of peanut production in Guilan province, Iran, and then the application of data envelopment analysis to determine optimum energy use pattern for saving energy and reduction of greenhouse gas emission. 120 peanut farms in Guilan province, Iran, an important hub for peanut production, are examined. Data envelopment analysis results show that 22 (18.33%) and 90 (75%) peanut producers are effective units based on constant and variable returns to scale, respectively. The technical, pure technical and scale efficiencies are 0.79, 0.98 and 0.81, respectively. The amount of energy consumption saving by converting inefficient farms into efficient farms is estimated to be 1760 Mega-Joule per hectare. Chemical fertilizer (contributing 48%) has the maximum share to total energy saving in peanut production. Therefore, correct and standard consumption of chemical fertilizer can be a viable solution for energy saving consumption. The total greenhouse gas emissions from peanut farming are computed to be 571.18 and 512.39 kg of carbon dioxide equivalent per hectare for present and optimum farms, respectively. Moreover, the total greenhouse gas emission can be reduced by 58.79 kg of carbon dioxide equivalent per hectare by optimizing energy inputs in peanut farming. This reduction in greenhouse gas emission can be realized by management of diesel fuel, nitrogen and machinery consumption according to optimized input rates introduced by the data envelopment analysis. [ABSTRACT FROM AUTHOR]

Conventional Data Envelopment Analysis (DEA) assesses the performance of Decision-Making Units (DMUs) by measuring only one type of efficiency using definite data. However, due to the vastness of some industries, such as banks, calculating only one type of efficiency cannot confirm DMUs as efficient or inefficient. Also, real-world cases are often faced with uncertain data, and conventional DEA lacks the power to consider these uncertainties in evaluation. In this paper, we develop a multi-objective DEA model to calculate three types of efficiencies, including profitability, operational, and transactional for bank branches when there are uncertain data. First, we apply a modified DEA model which is capable of calculating the common weight of all inputs and outputs by solving only one linear programming model. Then, we employ a robust approach to handle the uncertainty in data. The uncertainty in data is described with discrete scenarios. Discrete scenarios require a set of possible values for each parameter with uncertain nature. Finally, we apply a fuzzy programming method to convert the proposed multi-objective model into a single-objective one. Our main goal is to calculate three types of efficiencies for bank branches under four different scenarios. To validate the accuracy of the proposed model, a real case of 45 Agriculture bank branches located in West Azerbaijan in Iran is examined. The results show that the proposed model can produce accurate results under different scenarios. We also perform a comparative analysis on each efficiency aspect to specify the benchmark branches and also inefficient branches. Comparative analysis can help managers recognize where improvement should be prioritized. [ABSTRACT FROM AUTHOR]

This paper develops a theoretical background of the inverse DEA with pollution generating technology that is capable of dealing with undesirable outputs. Interaction between input, desirable output, undesirable output and environmental efficiency is investigated for industrial sector of Iranian provinces. Five scenarios are designed to perform this aim and construct a gradual planning scheme for short to long run CO 2 reduction. The following results are found. There exit a high potential of saving capital in short and mid and long-run from 80% to 150% in total. Province P8 and P5 are found as the most effective provinces in terms of saving inputs (labor and capital) in proposed scenarios. No labor reduction is suggested for a short run or for a long run in total, approximately 7%–160% extra labor is needed in the planning period. Some provinces face with infeasibility problem in the planning scheme especially in period on CO 2 reduction. The tightest scenario in terms of CO 2 reduction requires both extra labor and capital while reducing undesirable output and expanding desirable output. [ABSTRACT FROM AUTHOR]

• Propose a framework for measuring a maturity level of performance-based budgeting. • Develop a parallel network data envelopment analysis model. • Consider the hierarchical configuration of performance indicators. • Use fuzzy sets theory to deal with vagueness and ambiguity. • Present a case study to demonstrate the applicability of the developed framework. Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad-hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice. [ABSTRACT FROM AUTHOR]

• Efficiency of operational processes under uncertainty. • Developing a robust credibility DEA model with fuzzy perturbation degree. • Robust optimization approach is applied to consider uncertainty in constructing fuzzy sets. • An assessment of hospitals under uncertainty. Performance evaluation enables decision makers (DMs) to have a better view about the weaknesses and strengths of leading units to improve efficiencies as a crucial goal. Data envelopment analysis (DEA) is the most popular technique to measure performance efficiency of decision making units (DMUs). However, conventional DEA is unable to consider uncertainty of input and output data in the evaluations. In this study, in order to address uncertainty in data, a robust credibility DEA (RCDEA) model has been introduced. First, a fuzzy credibility approach is used to construct fuzzy data. Then, a robust optimization approach is applied to consider uncertainty in constructing fuzzy sets. Moreover, perturbation level is considered as exact and fuzzy values. To illustrate the capability of the proposed model, 28 hospitals are evaluated in northwestern region of Iran and results are analyzed. According to the results, as perturbation degree increases, DMUs get normalized lower efficiencies and vise-versa. [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]

Abstract: The objective of this study was to determine energy use efficiency in greenhouse cucumber production using a non-parametric production function. The method used to quantify energy use efficiency or, more specifically, excess input energy used, is data envelopment analysis (DEA). DEA creates a ‘‘best practice’’ production frontier based on the growers that produce their level of cucumber yield with the least amount of input energy. These greenhouses then serve as benchmarks against which the energy use inefficiency of all other growers can be measured. The data used in this study was obtained through a face-to-face questionnaire method in the surveyed region – Yazd province of Iran. The total energy input and energy output were calculated to be 1168023 and 124663MJha−1, respectively. According to this information, the energy productivity was 0.14kgMJ−1, and net energy value was estimated to be –1043360MJha−1 indicating energy has been lost in greenhouse cucumber production. Basic DEA models (CCR and BCC) were used to measure the technical efficiency of the growers based on seven energy inputs human labor, diesel fuel, FYM, fertilizers, chemicals, water for irrigation and electricity, and a single output included yield of cucumber (kgha−1). The CCR and BCC models indicated 10 and 25growers were efficient, respectively. The average values of overall technical efficiency, pure technical efficiency and scale efficiency of growers were found to be 0.8235, 0.9273 and 0.8880, respectively. The results also revealed that about 21% of the total input resources could be saved if the growers follow the input package recommended by the DEA. The maximum contribution to the total energy savings is 53% from diesel fuel, followed by electricity (27%). [Copyright &y& Elsevier]

Abstract: Conventional super-efficiency data envelopment analysis (DEA) models require the exact information of inputs or outputs. However, in many real world applications this simple assumption does not hold. Stochastic super-efficiency is one of recent methods which could handle uncertainty in data. Stochastic super-efficiency DEA models are normally formulated based on chance constraint programming. The method is used to estimate the efficiency of various decision making units (DMUs). In stochastic chance constraint super-efficiency DEA, the distinction of probability distribution function for input/output data is difficult and also, in several cases, there is not enough data for estimating of distribution function. We present a new method which incorporates the robust counterpart of super-efficiency DEA. The perturbation and uncertainty in data is assumed as ellipsoidal set and the robust super-efficiency DEA model is extended. The implementation of the proposed method of this paper is applied for ranking different gas companies in Iran. [Copyright &y& Elsevier]

Abstract: One of the primary concerns on target setting the electricity distribution companies is the uncertainty on input/output data. In this paper, an interactive robust data envelopment analysis (IRDEA) model is proposed to determine the input and output target values of electricity distribution companies with considering the existence perturbation in data. Target setting is implemented with the uncertain data and the decision maker (DM) can search the envelop frontier and find the targets based on his preference. In order to search the envelop frontier, the paper combine the DEA and multi-objective linear programming method such as STEM. The proposed method of this paper is capable of handling uncertainty in data and finding the target values according to the DM’s preferences. To illustrate ability the proposed model, a numerical example is solved. Also, the input and output target values for some of the electricity distribution companies in Iran are reported. The results indicate that the IRDEA model is suitable for target setting based on DM’s preferences and with considering uncertain data. [Copyright &y& Elsevier]

Abstract: Information technology (IT) is a tool crucial for enterprises to achieve a competitive advantage and organizational innovation. A critical aspect of IT management is the decision whereby the best set of IT projects is selected from many competing proposals. The optimal selection process is a significant strategic resource allocation decision that can engage an organization in substantial long-term commitments. However, making such decisions is difficult because there are lots of quantitative and qualitative factors to be considered in evaluation process. This paper has two main contributions. Firstly, it combines two well-established managerial methodologies, balanced scorecard (BSC) and data envelopment analysis (DEA), and proposes a new approach for IT project selection. This approach uses BSC as a comprehensive framework for defining IT projects evaluation criteria and uses DEA as a nonparametric technique for ranking IT projects. Secondly, this paper introduces a new integrated DEA model which identifies most efficient IT project by considering cardinal and ordinal data. Applicability of proposed approach is illustrated by using real world data of Iran Ministry of Science, Research and Technology. [Copyright &y& Elsevier]

Abstract: The contribution of this paper is to provide an approach for evaluating the performance of a group of decision making units (DMUs) based on the production technology. Group evaluation is an application of data envelopment analysis (DEA). DEA uses linear programming to provide a suitable technique to estimate a multiple-input/multiple-output empirical efficient function. This paper applies group evaluation to evaluate the performance of Iranian commercial banks. [Copyright &y& Elsevier]

Auditing energy usage of farming operations is a key step towards agricultural sustainability. The current systems of tomato production use a considerable quantity of energy. As a result, improving energy efficiency is a crucial stage in decreasing energy consumption in tomato production. Data envelopment analysis (DEA) model is an established methodology to assess energy efficiency in crop production. In this study, the bounded adjusted measure (BAM) is applied for improving the efficiency of tomato production as well as decreasing the carbon footprint. In this regard, the overall, environmental, production, and pure emission efficiency of tomato production in 24 provinces of Iran are investigated. The nine overall efficient tomato producing provinces recognized that showed they had no input excesses and/or output shortfalls. Also, similar to the overall efficiency, nine out of the 24 DMUs were recognized as environmental, production, and pure emission efficient. Finally, in order to measure the probable amounts of excessive investments in inputs, with the aim of obtaining more outputs, a new approach of determining congestion is proposed based on BAM model. [ABSTRACT FROM AUTHOR]

Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system's component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-indicators. This problem of ignoring the hierarchical structure of indicators weakens the discrimination power of NDEA models and may result in inaccurate efficiency scores. In this paper we propose a generalized fuzzy Multiple-Layer NDEA (GFML-NDEA) model and GFML-NDEA-based composite indicators (GFML-NDEA-CI) to incorporate the hierarchical structures of indicators in the ambit of the particular two-stage NDEA models. To demonstrate the usefulness of the GFML-NDEA-CI model proposed, its application was tested by evaluating the efficiency of the performance-based budgeting (PBB) system in 14 governmental agencies in Iran. The comparative analysis results obtained from the GFML-NDEA-CI (multi-layer) model with those from the single-layer fuzzy NDEA-CI model indicate that the number of efficient decision-making units (DMUs) in the one-layer model is eight, whereas it is solely one DMU in the multi-layer model. The discrimination power of the multi-layer model proposed is significantly increased by observing that standard deviation of efficiency scores are increased by 41%, 61%, and 84% for possibility levels 0, 0.5, and 1, respectively. This is obtained while reducing information entropy, thus suggesting that the proposed model yields more reliable scores. • We propose a generalized multilayer two-stage network DEA model. • Fuzzy set theory is used to represent the vagueness and ambiguity in the data. • We propose a generalized fuzzy multiple layer two-stage DEA based-CI model. • A case study demonstrates the applicability of the model. [ABSTRACT FROM AUTHOR]

Livestock production systems, such as dairy farming, are one of the most important contributors to resource use and if not managed well, it can be environmentally detrimental. Iranian livestock sector faces a variety of the challenges such as high costs of energy and environmental legislations as well as an increasing demand for dairy products to respond the growing population. This paper aims to contribute to the discussion on technical efficiency as a key indicator of energy use within dairy farming systems. A Window Data Envelopment Analysis (W-DEA) with energy use as inputs and milk production as output was modelled with data from 25 provinces during the last 22 years (1994–2016) in Iran. In addition, the Slack-Based Model (SBM) was used to compare the radial DEA model with non-radial SBM, both in a dynamic environment (window analysis). The average efficiency score of Iranian dairy farming production system was estimated at approximately 0.85. Through the years, three provinces including Zanjan, Ardabil and Hormozgan had the highest technical efficiencies. Window analysis represented that provinces are distinctive in terms of their technical efficiencies and energy consumption over the years. Applying the SBM model improved the accuracy of the estimated efficiency scores compared to the radial (DEA) model. Further analysis represented a significant difference between the technical efficiency of different milk production levels. Provinces that produced higher volumes of milk had lower technical efficiencies. Based on the results it can be concluded that there is a substantial space for upgrading the technical efficiency of dairy farming in Iran by improving resource use efficiency which leads to an optimized energy consumption. It is recommended to reform Iranian livestock farming policies by applying mechanized systems, optimal strategies for water, electricity and fossil fuel consumption, use of renewable energy and better feed management while enhancing milk productivity and technical efficiency. In this respect, it is suggested that policy makers consider different indicators such as energy use efficiency and environmental impacts when allocating subsidies and resources to different provinces and farms. • Iranian dairy farming was assessed for the technical efficiency over 22 years. • Efficiency of 25 provinces were identified using window data envelopment analysis. • The average efficiency score of dairy farming was approximately 0.85. • We propose improvement of resource management in order to minimize energy use. • Milk production was found as an effective factor on efficiency of provinces. [ABSTRACT FROM AUTHOR]

• Using the RPP approach to address the uncertainties in the SBSC management. • Considering the sustainable performance in the SC design of producing bioethanol from sugarcane. • Combining the RPP with fuzzy multi-objective programming to study the decision maker's preferences. • Using the FDEA for sourcing of the sugarcane feeds in Iran. • Considering disruption in the transportation routes of the sugarcane and fuel in the RPP model. Issues such as high fossil-fuel price fluctuations, considerable air pollution, and the realization that fossil sources are not unlimited threaten the global economy. Hence, researchers have been attracted by biomass, especially sugarcane, as a source of renewable energy to produce bioethanol and other biofuels. This research proposes a three-phase robust supply chain network design optimization model to produce bioethanol from sugarcane. Fuzzy integrated data envelopment analysis method is employed to select suitable cultivation lands as supply potential points. The model considers sustainability in its implementation so that the objectives are to maximize the profit, minimize environmental effects, and maximize social performance. Since some parameters are naturally uncertain, a robust possibilistic programming model is proposed, considering the possibility of transportation disruptions. The model performance has been illustrated through a case study in Iran. Finally, the results of the mathematical model have shown that the recommended supply chain design is justifiable. Practical strategic insights include recommendations for locating refineries in seven provinces (Gilan, Kermanshah, Hamedan, Semnan, Khorasan Razavi, Khouzestan, Kihkooliye). These results have been validated using a simulation-based approach, which demonstrates that the proposed model recommends decisions that more suitable than the deterministic model in terms of average and standard deviation of objective values. [ABSTRACT FROM AUTHOR]

• We propose a novel multi-criteria data envelopment analysis model. • The proposed two-stage DEA is first considered in the presence of ratio data. • Three states are considered (black-box, free-link, and fix-link). • A multi-criteria structure is used to demonstrate the validity of the models proposed. • An illustrative example on the efficiency of 60 sets of bank branches in Iran is provided. Data Envelopment Analysis (DEA) is a well-known technique for assessing efficiency levels of decision-making units (DMUs). Very often, available data may be expressed as ratios and, in such cases, traditional DEA models cannot be applied as long as biased efficiency results are produced, yielding the issues of efficiency underestimation and pseudo-inefficiency. In this paper, a novel two-stage MCDEA-R model to handle ratio data is developed observing three distinct assumptions – black-box, free-link, and fixed-link – offering a multi-criteria decision making (MCDM) perspective to the efficiency assessment problem in productive networks. While the proposed models are tested by evaluating the efficiency levels of a set of 30 bank branches in Iran, their distinctive features are highlighted in terms of previous literature to model ratio data under network structures. Precisely, there were not only gains in terms of mitigating pseudo-inefficiency and lack of discrimination power of weights issues, but there were also actual gains in terms of efficiency reliability as measured by information entropy. [ABSTRACT FROM AUTHOR]

Due to the competition among companies, evaluating the performance is one of the most important issues for managers. Data Envelopment Analysis (DEA) - Range Adjusted Measure model (RAM) is one of the non-radial models to evaluate the performance of companies. This paper presents a fully fuzzy network DEA-RAM model for evaluating airlines and extends the network DEA-RAM model in the fully fuzzy framework. First, a network structure is designed for airlines and then, a DEA-RAM model is presented for calculating efficiency scores. To handle uncertainty in data, fully fuzzy approach is considered and finally, a multi-objective network model is proposed to calculate airlines' efficiencies. To solve the proposed fully fuzzy DEA-RAM model, the lexicographic approach is used and the efficiency scores are reported as interval numbers. To illustrate the capability of the proposed fully fuzzy DEA-RAM model, the actual data of 14 Iranian airlines are gathered and the interval efficiencies of airlines are evaluated. Image 1 • The paper presents a network DEA-RAM developed as a novel fully fuzzy multi-objective model. • All inputs and outputs as well as the efficiency scores are considered as fuzzy numbers. • The proposed fully fuzzy network DEA-RAM model is applied to evaluate airlines in Iran. [ABSTRACT FROM AUTHOR]

The main problem of traditional methods of environmental impact assessment (EIA) is that in most of the existing algorithms and methods, such as Leopold, Folchi and RIAM, the main attention is to the destructive effects of the proposed plan, and the advantages of the industrial project are less noticeable. This has led to a permanent challenge between environmental organizations and industrial stakeholders. Data envelopment analysis (DEA) is a new approach of assessing the industrial units. Besides, it considers the positive economic and social impacts of the project and provides a comprehensive assessment of the industrial unit. With this approach, the environmental impacts of an industrial unit have been considered as "inputs" and its positive economic and social impacts considered as the "outputs" of the DEA models. Therefore, the problem of impact assessment changes into a DEA model. In the present study, the Alborz Sharghi Coal washing plant in northern Iran has been considered as a case study for implementing the DEA-EIA approach, and 19 plant activities and 11 environmental components have been used to evaluate the environmental effects of the plant. To solve the EIA problem, two commonly used DEA approaches, called CRS (constant returns to scale) and VRS (variable returns to scale), have been used. The DEA results identified the critical environmental components of the plant that should be considered seriously. Also, drawing the "potential improvement" diagram in the DEA method is an effective tool for determining the high risk activities of the factory and applying them in development plans. Besides, using the VRS model with maximize-output approach showed that some of the plant activities had the most differences with optimal mode and these components should be considered in future development plans. Finally, it can be concluded that, assessing the environmental impacts of the mineral industries with VRS maximize-output approach, is closer to the concept of sustainable development and cost-benefit analysis. Unlabelled Image • Data envelopment analysis (DEA) used as a new approach of EIA evaluations (DEA-EIA). • DEA-EIA considered sustainable development approach. • "Constant returns to scale" and "variable returns to scale" models used in DEA-EIA. • Potential Improvement analysis as a DEA results evaluate the plant's activities. • DEA-EIA determine more effective development plan and high-risk activities. [ABSTRACT FROM AUTHOR]

• Developing a mathematical model to optimize the design and planning of an algae-based biofuel supply chain network. • Considering biofuel price as a variable which effects on demand under alternative fuel price uncertainty. • Taking deterioration into account in the algal biofuel supply chain network design. • Presenting an environmental objective function which maximizes the carbon absorption in a multi-period time horizon. • Proposing DEA approach to rank the candidate cities based on efficiency for microalgal harvesting. According to the high sugar and oil content of algae, the algal biofuel have been taken into consideration in recent years. This paper develops a multi-objective mixed integer quadratic programming (MMIQP) model to optimize the design and planning of an algae-based biofuel supply chain network under environmental and economic objectives. The goals of this paper are set to maximize the total profit and carbon dioxide absorption of the supply chain. The proposed mathematical model considers the deterioration issue in the inventory and during the transportation, in a multi-period time horizon. Since the alternative fuel price is not certain in the real world, a two-stage stochastic programming is utilized to address this uncertainty. Moreover, data envelopment analysis (DEA) approach is proposed to rank the candidate cities based on efficiency for microalgal harvesting. The applicability and efficiency of the proposed model are finally demonstrated through a real case study in Iran. [ABSTRACT FROM AUTHOR]