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The purpose of this study identified the priorities, challenges and different aspects of the mitigation phase according to policymakers for planning and better management of reducing risk within the cultural, religious, social and political conditions of Iran.

In the present qualitative study, 19 policymakers in different levels of the disaster management organizations were selected based on purposive sampling. Semi-structured and face-to-face interviews were used to identify the participants’ views. The findings were analyzed using thematic analysis.

The present situation and the challenges of the mitigation phase were the main themes in this study. Risk perception, training and media were the sub-themes. Political, legal, social and cultural challenges of the mitigation phase were also sub-themes.

The findings of this study indicated that different aspects should be considered to minimize the risk of earthquake. In addition, all kinds of media, including visual, written, audio, instrumental, group and multimedia, should be used for enhancing public awareness so that readiness for earthquakes can be considered as a permanent mission of the citizens, who are always concerned about earthquakes.

Earthquake usually causes death, injury, disability and destruction of buildings and infrastructure, and people with disabilities are usually affected more than healthy people. As undesirable experiences may also have positive outcomes, this study aims to investigate the experiences of PWD and identify the positive effects of earthquakes on them in Iran, as an earthquake-prone country.

In this qualitative study, 20 participants were selected purposively among those having physical disability, aged 23-55 years and with experience of an earthquake. Their opinions were collected using semi-structured interviews. Analysis was performed using thematic approach and MAXQDA software was used to organize the data.

The positive effects of earthquake were categorized into five main themes: promotion of preparedness, knowledge enhancement, improvement of structures, socio-economic improvement (economic situation enhancement and social cohesion promotion) and outstanding role of national and international non-governmental organizations.

Although disasters are generally unpleasant, in the long term, they can result in positive effects and may be considered as opportunities to improve the situation and eliminate certain limitations. It is also important to learn from experiences of people with disabilities and apply the lessons learned, for enhancing preparedness and providing better services in the response phase of disaster management. Additionally, paying attention to the positive attitudes of such people, with special conditions and limitations, indicates their enhanced resilience to cope with disasters and emergencies, including COVID-19, which should be taken into consideration by policymakers and planners in future programs.

The vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.

In this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).

The results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.

In this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

In the context of overcoming malnutrition in elementary school children and increasing public awareness of this issue, the Indonesian Government has created a “Card for Healthy School Children” (KMS-AS) program in the form of a paper health card. However, currently, the KMS-AS record data are still written on paper, which is less effective in terms of the health process. An integrated measuring device and an online data-recording system are needed to promote children’s health and facilitate access and transfer of data from one place to another. This study aims to develop NFC and IoT-based KMS-AS using sensor fusion method.

The results of this study show that an integrated measuring device for weight, height, body temperature and Spo2 level can be connected with mobile and Web applications using IoT technology, facilitating data recording and monitoring of children’s nutritional status. The sensor fusion method was used for the classification of nutritional status and health status, based on the results of measurement tools. Near field communication (NFC) technology was used to facilitate user identification when making measurements.

The results show that KMS-AS can facilitate classification of nutritional status and children's health status. Measurement and classification data can be monitored via Web and mobile applications. The accuracy of height, weight, body temperature and Spo2 measurements was 98.21%, 98.59%, 98.93% and 98.93%, respectively.

In this research, the authors successfully produced a system using sensor fusion method for measuring body weight, height, temperature and Spo2 level, which is integrated and can be connected to mobile applications and the Web using the IoT and NFC.

Within a religious tourism destination, the study of business networks can be used to improve understanding of the interactions taking place within or among businesses, by exploring the informal and formal connections linking them together. Hence, the purpose of this study is to map and analyze strategic business networks operating in a religious destination.

This research explores the networks of stakeholders associated with a religious context in an intensive single case study on Al-Atabat Al-Aliyat in Iraq through qualitative analysis. Multiple methods of data collection were used to build strategic networks promoting religious tourism in the Middle-East region. Key analysis practices include content analysis and social network analysis (SNA). The identified interrelationships between stakeholders were analyzed by using UCINet, an SNA tool.

The study reveals three different types of cooperation networks in the case of Al-Atabat Al-Aliyat, namely, a 67-actor continuous network, a 69-actor non-Arbaeen network and an 89-actor Arbaeen network. The results also pinpoint the need for more collaborative activities on the different levels of cooperation in the networks.

This paper provides important guidelines on how to manage the interrelationships within tourism networks in a religious destination such as Iraq. The findings highlight the acute need for establishing strategic and effective networks of stakeholders in Al-Atabat Al-Aliyat. Non-tangible, long-term and quantitative benefits can be found for tourism businesses investing time and money in religious networks in Iraq.

The research contributes to religious tourism literature, filling the gaps about how the interrelationships among the actors involved in a business network can evolve over time and influence the success of a certain religious destination.

This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type.

In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem.

As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms.

The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability.

The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.

The increasing prevalence of coronavirus disease (COVID-19) is a global crisis that leads to physical and psychological outcomes for health-care workers, so this study aims to investigate the mental health outcomes (including general health, generalized anxiety disorder and posttraumatic stress disorder) in health-care workers in Rafsanjan, Iran.

By using convenience sampling, this cross-sectional study was conducted on 332 health-care workers working in public hospitals in southern Iran. Data collection lasted from March to April 2020. General Health Questionnaire (GHQ-28), Generalized Anxiety Disorder 7-item (GAD-7) and Impact of Event Scale were used to collect data. The data were then analyzed by using SPSS 25 and descriptive and inferential statistics (chi-square and multivariate logistic regression).

In total 45.5% of the participants had psychological disorder according to GHQ. In addition, 25.3% of the participants had GAD and 31.6% had posttraumatic stress disorder (PTSD). The results using multivariate logistic regression showed that only income was significantly associated with psychological disorders (95% confidence interval for odds ratio: 1.32–6.45, P = 0.008).

According to the results, the incidence of GAD and PTSD was high among health-care workers. Therefore, it is recommended that the psychological skills of health-care workers be strengthened through counseling and training programs.

This paper provides a novel analysis of mental health in health-care workers in Iran.

One of the practical issues in the area of location and allocation is the location of the hub. In recent years, exchange rates have fluctuated sharply for a number of reasons such as sanctions against the country. Natural disasters that have occurred in recent years caused delays in hub servicing. The purpose of this study is to develop a mathematical programming model to minimize costs, maximize social responsibility and minimize fuel consumption so that in the event of a disruption in the main hub, the flow of materials can be directed to its backup hub to prevent delays in flow between nodes and disruptions in hubs.

A multi-objective mathematical programming model is developed considering uncertainty in some parameters, especially cost as fuzzy numbers. In addition, backup hubs are selected for each primary hub to deal with disruption and natural disasters and prevent delays. Then, a robust possibilistic method is proposed to deal with uncertainty. As the hub location-allocation problem is considered as NP-Hard problems so that exact methods cannot solve them in large sizes, two metaheuristic algorithms including a non-dominated sorting genetic algorithm non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied to tackle the problem.

Numerical results show the proposed model is valid. Also, they demonstrate that the NSGA-II algorithm outperforms the MOPSO algorithm.

The proposed model was implemented in one of the largest food companies in Iran, which has numerous products manufactured in different cities, to seek the hub locations. Also, due to several reasons such as road traffic and route type the difference in the rate of fuel consumption between nodes, this model helps managers and decision-makers to choose the best locations to have the least fuel consumption. Moreover, as the hub set up increases the employment rate in that city and has social benefits as it requires hiring some staff.

This paper investigates the hub location problem considering backup hubs with multiple objective functions to deal with disruption and uncertainty. Also, this study examines how non-hub nodes are assigned to hub nodes.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.

In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.

Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.

The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.