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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.

This study aims to review the results of relevant studies to shed light on social trust-building in different contexts and the factors that affect it in disaster risk management.

This systematic review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses model. The study keywords were searched for in PubMed, Scopus and Web of Science databases on August 2021. The inclusion criteria were English-written articles published in social trust and disaster relief efforts. Exclusion criteria were lack of access to the full text and article types such as nonoriginal articles.

Out of 1,359 articles found, 17 articles were included in the final analysis using four general categories: six articles on the role of local government in trust-building (local governments), five articles on the role of social media in trust-building (social media), four articles on the role of social capital in trust-building (social capital) and two articles on the importance of community participation in trust-building (community participation).

Understanding the role of social trust and the factors which influence it will help the development of community-based disaster risk management. Therefore, disaster management organizations and other relief agencies should take the findings of this study into account, as they can help guide policymaking and the adoption of strategies to improve public trust and participation in comprehensive disaster risk management. Further studies recommended understanding people’s experiences and perceptions of social trust, relief and disaster preparedness.

This study aims to investigate the efficacy of Ag@GO/Na₂SiO₃ nanocomposite in eliminating As from aqueous solutions. Employing response surface methodology, the research systematically examines the adsorption process.

Various experimental parameters including sample pH, contact time, As concentration and adsorbent dosage are optimized to enhance the As removal process.

Under optimized conditions, the initial As concentration, contact time, pH and adsorbent dosage are determined to be 32 ppm, 50 mins, 6.5 and 0.4 grams, respectively. While the projected removal of As stands at 97.6% under these conditions, practical application achieves a 93% removal rate. Pareto analysis identifies the order of significance among factors as follows: adsorbent dosage > contact time > pH > As concentration.

This study highlights the potential Ag@GO/Na₂SiO₃ as a promising adsorbent for efficiently removing industrial As from aqueous solutions, and it is likely to have a good sufficiency in the filtration of water and wastewater treatment plans to remove some chemical pollution, including paints and heavy metals.

The simplicity of the nanocomposite preparation method without the need for advanced equipment and the cheapness of the raw materials and its potential ability to remove As are the prominent advantages of this research.

One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.

One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.

The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

The purpose of this paper is to manage the demand uncertainty considered as lower and upper levels for a medium-scale industrial distribution planning problem in a biobjective inventory routing problem (IRP). In order to achieve this, the grey system theory is applied since no statistical distribution from the past data and incomplete information.

This study is investigated with optimizing the distribution plan, which involves 30 customers of 12 periods in a manufacturing company under demand uncertainty that is considered as lower and upper levels for a biobjective IRP with using grey demand parameters as a grey integer programming model. In the data set, there are also some missing demand values for the customers. So, the seven different grey models are developed to eliminat the effects on demand uncertainties in computational analysis using a piece of developed software considering the logistical performance indicators such as total deliveries, total cost, the total number of tours, distribution capacity, average remaining capacity and solution time.

Results show that comparing the grey models, the cost per unit and the maximum number of vehicle parameters are also calculated as the new key performance indicator, and then results were ranked and evaluated in detail. Another important finding is the demand uncertainties can be managed with a new approach via logistics performance indicators using alternative solutions.

The results enable logistics managers to understand the importance of demand uncertainties if more reliable decisions are wanted to make with obtaining a proper distribution plan for effective use of their expectations about the success factors in logistics management.

The study is the first in terms of the application of grey models in a biobjective IRP with using interval grey demand data. Successful implementation of the grey approaches allows obtaining a more reliable distribution plan. In addition, this paper also offers a new key performance indicator for the final decision.

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.