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To achieve the optimum performance of electric transmission power system performance, the possibility of generators’ failure and the consequences are amongst the most important and real assumptions which should be taken into consideration. This paper aims to recognize the most influential factors on generators’ failures that can have a deep effect on the total cost and environmental issues. The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.

The data used in this study are gathered from a real case in USA in first step, the influential generator points that their failure has a significant effect on the objective function, have been recognized. Then, different failure scenarios are defined, and the optimum values in each of these scenarios through the GAMS modeling software are found. Consequently, by using a two-level factorial design approach, the critical generators across the power grid are determined.

The results show that by using such information, it is possible to detect the significant nodes in the power system grid and have a better maintenance plan. In addition, by means of this analysis and changing the capacity of main generators, it is possible to significantly reduce the operation costs. By comparing the indexes in case of the generator’s location, it seems that some of them are critical because of their capacity and position in the network (as their failure causes infeasibility in the model). Also, some of these deficiencies caused considerable index changes and critical consequences.

The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.

This paper endeavors to recognize the most influential factors on generators’ failures that can have a deep effect on the total cost and environmental issues.

The integrated proposed approach is useful for investigating the generators’ failure effects on the performance of electric power transmission grids from the economic and environmental perspectives. In other words, the cost and pollution minimization policies are considered to decrease the unfavorable generators’ failure effects on electric power flow.

This study aims to present an integrated approach based on resilience engineering (RE) for layout optimization of a maintenance workshop in a large transmission unit.

Moreover, optimum facility layout design (FLD) is identified by RE, fuzzy simulation and fuzzy data envelopment analysis.

RE is considered in context of flexibility and redundancy. Results indicated that U-Shaped layout with flexible facilities increases the efficiency of the maintenance workshop.

This is the first study that considers RE for optimization of FLD in a gas transmission unit. RE significantly increases safety features of hazardous systems such as a gas transmission unit.

Resilience engineering, job satisfaction and patient satisfaction were evaluated and analyzed in one Tehran emergency department (ED) to determine ED strengths, weaknesses and opportunities to improve safety, performance, staff and patient satisfaction. The paper aims to discuss these issues.

The algorithm included data envelopment analysis (DEA), two artificial neural networks: multilayer perceptron and radial basis function. Data were based on integrated resilience engineering (IRE) and satisfaction indicators. IRE indicators are considered inputs and job and patient satisfaction indicators are considered output variables. Methods were based on mean absolute percentage error analysis. Subsequently, the algorithm was employed for measuring staff and patient satisfaction separately. Each indicator is also identified through sensitivity analysis.

The results showed that salary, wage, patient admission and discharge are the crucial factors influencing job and patient satisfaction. The results obtained by the algorithm were validated by comparing them with DEA.

The approach is a decision-making tool that helps health managers to assess and improve performance and take corrective action.

This study presents an IRE and intelligent algorithm for analyzing ED job and patient satisfaction – the first study to present an integrated IRE, neural network and mathematical programming approach for optimizing job and patient satisfaction, which simultaneously optimizes job and patient satisfaction, and IRE. The results are validated by DEA through statistical methods.

Construction materials comprise a major part of the total construction cost. Given the importance of bitumen as a fundamental material in construction projects, it is imperative to have an accurate forecast of its consumption in the planning and material sourcing phases on the project. This study aims to introduce a flexible genetic algorithm-fuzzy regression approach for forecasting the future bitumen consumption.

In the proposed approach, the parameter tuning process is performed on all parameters of genetic algorithm (GA), and the finest coefficients with minimum errors are identified. Moreover, the fuzzy regression (FR) model is used for estimation. Analysis of variance (ANOVA) is used for selecting among GA, FR or conventional regression (CR). To show the applicability of the proposed approach, Iran’s bitumen consumption data in the period of 1991-2006 are used as a case study.

Production, import, export, road construction and price are considered as the input data used in the present study. It was concluded that, among all the forecasting methods used in this study, GA was the best method for estimating.

The proposed approach outperforms the conventional forecasting methods for the case of bitumen which is a fundamental economic ingredient in road construction projects. This approach is flexible, in terms of amount and uncertainty of the input data, and can be easily adapted for forecasting other materials and in different construction projects. It can have important implications for the managers and policy makers in the construction market where accurate estimation of the raw material demand is crucial.

This is the first in this field introducing a flexible GA-FR approach for improving bitumen consumption estimation in the construction literature. The proposed approach’s significance has two folds. Firstly, it is completely flexible. Secondly, it uses CRs as an alternative approach for estimation because of its dynamic structure.

The purpose of this paper is to review current literature analyzing human factors in maintenance, and areas in need of further research are suggested.

The review applies a novel framework for systematically categorizing human factors in maintenance into three major categories: human error/reliability calculation, workplace design/macro-ergonomics and human resource management. The framework further incorporates two well-known human factor frameworks, i.e., the Swiss Cheese model and the ergonomic domains framework.

Human factors in maintenance is a pressing problem. The framework yields important insights regarding the influence of human factors in maintenance decision making. By incorporating various approaches, a robust framework for analyzing human factors in maintenance is derived.

The framework assists decision makers and maintenance practitioners to evaluate the influence of human factors from different perspectives, e.g. human error, macro-ergonomics, work planning and human performance. Moreover, the review addresses an important subject in maintenance decision making more so in view of few human error reviews in maintenance literature.

Reverse logistics refer to processes related to the reuse of products. The role of suppliers’ performance is crucial in achieving quality, cost, service and delivery aims of a supply chain. The selection of suppliers is regarded as one of the critical issues encountered by purchasing and operations managers in a supply chain to enhance organization’s global marketplace competitiveness. Most of the supply chain models are rather complex problems. Consequently, it is impossible to propose systematic models to handle them. Therefore, in this paper a new integrated approach based on experimental design and computer simulation is proposed for supplier selection. The paper aims to discuss these issues.

In this study, a simulation approach is implemented to determine certain equivalent of parameters values in a CLSC network design which cannot be computed through mathematical model. Suppliers’ order quantities are investigated by Taguchi method for planning time horizon. Moreover, data envelopment analysis (DEA) is applied to assess suppliers based on quality, cost, delivery time, production capabilities, services and technology.

In the numerical example, there are three suppliers for different regions. Purchase value of each supplier is measured in three years successively. According to the proposed method, the authors find the minimum level of costs together with the maximum number of high-quality products.

The objective functions of model are minimizing the costs and maximizing number of high-quality products. The integrated approach introduced in this paper enables managers to select their suppliers effectively in their real system.

Most supply chain models are complex and the identification of proper and optimal solutions in complex real-world systems often requires the solution of multi-objective problems involving multiple stochastic variables. Therefore, the paper introduces a new integrated approach for supplier selection in closed loop supply chain. To the best of the authors knowledge, this is the first study that integrates DEA, computer simulation and Taguchi method for supplier selection in closed loop supply chains.

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.

Wind as a major source of renewable energy has received tremendous attentions due to its unique features to reduce carbon emission and also to keep the environment safe. Nevertheless, to use wind energy properly, the environmental circumstances and geographical location related to wind intensity should be considered as a priority. Different factors may affect the selection of a suitable location for developments of wind power plants; thus, these factors should be considered concurrently to identify the optimum location of wind plants.

In this study, first, basic data envelopment analysis (DEA) was used, then dual DEA was used and, finally, Anderson Petersen (AP) model of dual DEA was selected to prioritize cities or decision-making units (DMUs). Numerical Taxonomy (NT) method was also used to assess the validity of AP dual model in DEA. The prescribed approach was applied for five cities in East Azerbaijan province of Iran.

The results indicate that wind power as a renewable energy can be harnessed in few cities, and the ranking by DEA illustrated that the city of Tabriz is the first priority.

Low environmental degradation effects in comparison to other methods and the ability to utilization at a widespread level include the benefits of using wind energy in the generation of electricity. In this regard, the study of relevant potentials and finding suitable locations for the deployment of wind energy utilization equipment are essential. Using DEA method helps us to choose optimal locations according to different criteria.

Wind energy is justifiable in reducing social costs in comparison with fossil fuel plants, which includes negative effects, and its electricity can be used as a sustainable energy in the country's economic, social and cultural development.

For identifying the most proper location for development of wind power plants in Iran, DEA is applied for the first time to prioritize the suitable locations for installations of wind turbines among five different cities in the East Azerbaijan region. A number of crucial factors including land price, distance to power, rate of natural hazards, wind speed and topography are considered for location optimization of wind turbines for the first time. Also, to validate the results of DEA method, NT method is used to assess the validity of AP dual model in DEA.

Increased dietary salt content is one of the effective factors of hypertension and a major public health challenge globally. Although the positive effects of dietary salt reduction on health are universally accepted, people can hardly reduce their salt intake. The purpose of this study is to identify the inhibitory factors of dietary salt reduction among 20–65-year-old women in Yazd City, Iran.

This study was conducted using a deductive content analysis approach based on the communication for the behavioral impact (COMBI) framework. The purposeful sampling method was applied with maximum variation in terms of different educational levels, age groups, occupational status and residential areas to select the participants. Snowball sampling was used to select health-care professionals. Furthermore, semi-structured interviews and focus-group discussions were conducted with 31 local women and 11 health-care professionals working in the City until data saturation was achieved. Data were analyzed using Graneheim and Landsman’s method.

After data analysis, 617 initial codes were extracted over the perceived barriers. After merging similar codes, 223 codes were extracted. The barriers were classified into five main categories of family, personal, organizational, educational and socio-cultural barriers.

Based on the COMBI framework, the results demonstrated that the most important barriers for reducing salt intake were negative attitude toward restrictions on dietary salt intake, insufficient and incorrect beliefs about the health risk of salt, lack of family support, inadequate health literacy and low self-efficacy in Yazd City. Among these barriers, lack of family support was considered as the most effective factor in reducing salt consumption. So, by focusing on this area and providing the community with the required education, the amount of salt consumed by families can be reduced.

The purpose of this paper is to assess the quality of Iranian university libraries.

This first systematic review and meta-analysis were based on the PRISMA guidelines by searching in national and international databases from 2003 to January 2017 with standard Persian and English keywords. Data searching, extracting and quality appraising were completed by two researchers, independently. Any unexpected documents were assessed by a third expert researcher. Data were extracted in accordance with the “Strength of the Reporting of Observational Studies in Epidemiology” checklist after the final selection of appraised documents. Random effects size based on Cochrane test and I² were used for combining the obtained results from different studies together by considering the heterogeneity of studies.

Based on the meta-analysis conducted in 25 (6.42 percent) included studies, the total sample size was estimated. According to three dimensions of LibQUAL, findings of current information control, affect of service and the library as a place were estimated as 5.37 [CI95%: 5.02, 5.73], 6.91 [CI95%: 5.56, 6.26], and 5.46 percent [CI95%: 5.2, 5.73], respectively. Also, mean of service adequacy and superiority gap are equal to 0.07 [CI95%: −0.22, 0.36] and −2.06 [CI95%: −2.89, −1.23], respectively. There was a significant correlation between three dimensions of service quality and service superiority gap of LibQUAL and geographical regions of Iran (p<0.01). Also, a significant correlation was found between the gaps of services and three aspects of LibQUAL model and published years through a meta-regression test (p<0.01).

The results obtained from the present study showed that users are relatively satisfied with the quality of services provided by Iranian university libraries. An improvement in the quality of library services can promote the scientific level of universities.

The results of the present systematic review and meta-analysis study demonstrate a vital connection between primary research studies and decision-making for policymakers in Iranian university libraries to increase quality services.