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Quantifying and controlling the quality characteristics of parts produced by additive manufacturing (AM) processes has attracted significant interest in the research community. However, to increase the sustainability of AM processes, such quality characteristics need to be assessed together with life cycle performance of AM processes such as energy and material consumption and manufacturing cost. Although a few studies have been performed for several quality characteristics, i.e. surface roughness and tensile strength, the relationship between dimensional performance and manufacturing cost is still not well known for AM processes.

In this paper, a comprehensive study of the dimensional performance and manufacturing cost of fused deposition modeling AM process is performed. Design of experiment technique is used, and the correlation of different cost components and the dimensional accuracy of parts are statistically studied.

The optimum process parameters for simultaneously optimizing the dimensional performance and manufacturing cost are identified. The analysis shows that as opposed to traditional manufacturing processes, obtaining a better dimensional performance is not necessarily associated with higher cost in the AM processes.

Almost no study and analysis for the combined dimensional performance and manufacturing cost has been performed for AM processes in the literature. It is known that within the context of traditional manufacturing processes, a natural trade-off governs the pursuit of higher dimensional performance and the manufacturing cost. However, as the AM process has a different nature compared with traditional manufacturing processes, the relationship between manufacturing cost and dimensional performance of parts has to be studied. Understanding this relationship will also help to establish a cost-optimal and sustainable tolerance allocation strategy in assemblies with AM components.

Concern for health, safety and environment (HSE) is increasing in many developing countries, especially in energy industries. Improving power plants efficiencies in terms of HSE issues requires considering these issues in performance assessment of power generation units. This study aims to discuss the use of data envelopment analysis methodology for the performance assessment of electrical power plants in Iran by considering HSE and conventional indicators.

Installed capacity, fuel consumption, labor cost, internal power, forced outage hours, operating hours and total power generation along with HSE indices are taken into consideration for determining the efficiency of 20 electric power plants or decision-making units (DMUs). Moreover, DMUs are ranked based on their relative efficiency scores.

Results show that HSE factors are significant in performance assessment of the power plants studied in this research, and among HSE factors, health has the most powerful impact on the efficiency of the power plants.

The approach of this study could be used for continuous improvement of combined HSE and conventional factors. It would also help managers to have better comprehension of key shaping factors in terms of HSE.

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.

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.

With the rising needs of health-care (HC) services during the health outbreaks, it is essential to upgrade the existing HC delivery infrastructure. The study aims to prioritize and highlight the interrelationships among the key factors of the smart health-care supply chain (HCSC) by implementing the concept of Industry 4.0.

The key factors of implementing Industry 4.0 in HCSC have been identified through extensive literature review and stakeholders' opinions. To achieve the abovementioned objectives, the present study proposed hybrid multi-criteria decision-making (MCDM) tools, namely, the fuzzy-analytic hierarchy process (AHP) and fuzzy- decision-making trial and evaluation laboratory (DEMATEL). The Fuzzy-AHP prioritized the factors of implementing Industry 4.0 in HCSC, while the cause–effect relationships among the factors have been explored using fuzzy-DEMATEL.

The results of the study indicated that HC logistics management (HCLM) is the most prioritized factor of implementing Industry 4.0 in HCSC, followed by the integrated HCSC, then sustainable HCSC practices, HCSC innovation and technological aspects, HCSC institutional perspectives, HCSC competitiveness, social aspects and economic factors of HCSC. The cause–effect analysis has highlighted integrated HCSC, HCLM, HCSC competitiveness and social aspects as the cause group factors and they are the critical success parameters for implementing Industry 4.0 in the HCSC.

The results of the study can be useful for policymakers, humanitarian organizations, health administrators and other decision makers considering the smartening of HCSC to enhance the operational performance of health facilities.

This is one of the few studies to have been conducted so far in which the subfactors of HCSC implementing Industry 4.0 have been identified and analyzed using the fuzzy-AHP and fuzzy-DEMATEL hybrid approach.

The purpose of this study is to propose a novel super-efficiency DEA model to appraise the relative efficiency of DMUs with zero data and stochastic data. Our model can work with both variable returns to scale (VRS) and constant returns to scale (CRS).

This study proposes a new stochastic super-efficiency DEA (SSDEA) model to assess the performance of airlines with stochastic and zero inputs and outputs.

This paper proposes a new analysis and contribution to the knowledge of efficiency assessment with stochastic super-efficiency DEA model by (1) using input saving and output surplus index for efficient DMUs to get the optimal solution; (2) obtaining efficiency scores from the proposed model that are equivalent to original stochastic super-efficiency model when feasible solutions exist. A case study is given to illustrate the applicability of our proposed model. Also, poor performance reasons are identified to improve the performance of inefficient airlines.

For the first time, a new SSDEA model for ranking DMUs is proposed. The introduced model produces a feasible solution when dealing with zero input or output. This paper applies the input saving and output surplus concept to rectify the infeasibility problem in the stochastic DEA model.

This paper aims to review the healthcare supply chain (HSC) literature along various areas and to find out the gap in it.

In total, 143 research papers were reviewed during 1996-2017. A critical review was carried out in various dimensions such as research methodologies/data collection method (empirical, case study and literature review) and inquiry mode of research methodology (qualitative, quantitative and mixed), country-specific, targeted area, research aim and year of publication.

Supply chain (SC) operations, performance measurement, inventory management, lean and agile operation, and use of information technology were well studied and analyzed, however, employee and customer training, tracking and visibility of medicines, cold chain management, human resource practices, risk management and waste management are felt to be important areas but not much attention were made in this direction.

Mainly drug and vaccine SC were considered in current study of HSC while SC along healthcare equipment and machine, hospitality and drug manufacturing related papers were excluded in this study.

This literature review has recognized and analyzed various issues relevant to HSC and shows the direction for future research to develop an efficient and effective HSC.

The insight of various aspects of HSC was explored in general for better and deeper understanding of it for designing of an efficient and competent HSC. The outcomes of the study may form a basis to decide direction of future research.

Considering the direct impact of retailers' performance on the economy, this paper aimed to propose a comprehensive framework to evaluate the performance of different branches of a retailer.

Through a case study, the weights of indicators were calculated by the best-worst method (BWM) and the branches' performance was appraised using data envelopment analysis (DEA).

The branches were ranked in terms of performance, and sensitivity analysis and statistical tests were conducted to realize the weaknesses and strengths of the branches. Then, some strategies were proposed using strengths, weaknesses, opportunities and threats (SWOT) analysis to improve the performance of the weak branches.

This paper contributes to previous studies on the evaluation of retailers' performance by proposing a triple framework based on resilience, sustainability and sales-marketing indicators. This paper focused on branches' operations and branches' optimization by improving performance in terms of these three indicators. This paper also offers a qualitative and quantitative analysis of retailers' performance, which has received less attention in previous studies.