The purpose of the current research was to investigate the obstacles to the implementation of school-based management in Iran's educational system based on the interpretive structural modeling approach. The research method was a modeling study. The statistical population of the research was specifically the academic staff members of the university, 25 of whom were purposefully selected from the desired cases. The data collection tool was a researcher-made self-interactive questionnaire. In order to measure and evaluate the validity of the questionnaire, the criterion of content validity was used. The content validity of the questionnaire was 0.96. The data analysis was done with interpretive structural modeling method. The results of the research showed that 9 factors play a role as barriers to school-oriented management. Also, the results showed that financial obstacles (the cost of school-oriented management), socio-cultural obstacles and political obstacles (political focus on schools by the central government) as key independent variables have high influence power and low dependence. Also, the results showed that the lack of belief in the professional competence of school administrators, the lack of infrastructure facilities, the emphasis of upper management on the supervision and control of schools, and the lack of cooperation of local communities with school-based management as the linking variables with the greatest power of influence and the greatest power of dependence. and the centralism in the education system of the country and the lack of legal powers to school managers and staff as dependent variables have high dependency power and low influence power.

Background: Nowadays, attention to the ethical dimensions of decision-making issues at different levels of national, regional or enterprise has become an undeniable issue. Developments such as globalization, community development, the emergence and development of large multinational companies, financial markets and etc. intertwined interests of the communities that didn’t have noticeable influence on each other before. Hence the result of our actions and decisions in different communities affects other communities and also future generations more than ever. Considering the ethical issues in operations research as a powerful tool in decision making, helps policy makers and managers to pay attention to the interests of all stakeholders in their decision.Methods: In this review paper, after an introduction to concept and dimensions of ethical issues in management science and decision making, necessary prerequisite for modeling problems with ethical dimensions are described and the application of operations research (OR) in modeling these issues explained. And specifically, the Agent Based Modeling (ABM) method as a powerful technique in modeling such problems is introduced.Results: Most of the classical OR methods commonly used in optimization problems cannot be used for new issues with ethical content and therefore newer methods and tools for modeling and solving such problems should be develop. In this article Agent Based Modeling (ABM) method is suggested for modeling the ethical and social sciences problems.

The immune system plays a critical role in protecting the human body against various pathogens and diseases. Understanding the complexity and dynamics of the immune system is essential for developing effective therapies and interventions. Agent-based modeling (ABM) has emerged as a powerful tool for simulating and studying the behavior of complex systems, including the immune system. This review examines the advantages, challenges, and applications of ABM in immune system modeling. ABM captures the complexity of immune cell behavior, spatial effects and stochasticity. It has been applied to study immune cell dynamics, immune responses to pathogens, immune cell migration, immunotherapies and immune system disorders. Challenges include parameterization, validation, and computational resource requirements. Future directions involve integrating multi-omics and single-cell data, incorporating machine learning, exploring multi-scale modeling, and developing user-friendly interfaces. ABM holds promise for enhancing our understanding of immune system dynamics and advancing diagnostics and treatments in immunology.

Electricity is one of the most significant energy sources in the modern world. Over the last century, there has been no significant change in the centrally controlled structure of electrical power grids, especially in developing countries. Global population and economic expansion, together with air pollution, put further strain on the electricity industry. The power electrical grid, as the main structure for power transmission, has to reconsider its concepts. Currently, critical peak load caused by residential customers has attracted utilities to pay more attention to residential demand response (RDR) programs. With the rise in household computing power and the increasing number of smart appliances, more and more residents can participate in demand response (DR) management through the home energy management system (HEMS) to prioritize the start-up of electrical appliances according to the necessity of use and efficiency. This research is an applied case study designed for cold regions with an average household population of three people. It is suggested that, in addition to, time of consumption and household type, the cluster of appliances affects the price of consumption, and the cost paid by users varies depending on the cluster of appliances used by different households at different times. To evaluate the potential for changing prices to better consumption criteria, a multi-agent hierarchical model including utility and different types of households and appliances is presented in this study that takes into consideration two main objectives, including peak smoothing and energy consumption reduction. Based on the specified indicators, the analytical results of two scenarios were analyzed, and it was concluded that variable pricing of appliance consumption can reduce electricity consumption and smooth the peak load curve.

The prediction of the results of introducing a new product into the market is one of the vital issues the organization executives face before investing in marketing activities. The impact of various factors on the market as well as the specific characteristics of the market, depending on the region and its product type, has made it difficult to predict market behavior. In Iran, retailers are effective players, especially in the FMCG market. The objective of this paper was to suggest a model to the marketing managers to predict the result of launching their new product to market considering their special market attributes. Agent-based modeling, as a tool for modeling complicated systems, can be helpful for simulating real-world conditions. In the present paper, agent-based modeling was used to model the market for agents including brand owners, retailers and consumers with particular profit functions. The introduction over a three-year period of a new soft drink in the Iranian market is considered as a case study. The results showed that taking into account the needs of retailers and consumers simultaneously and changing policies based on long-term profitability make the new product diffusion process successful.

In this paper we try to simulate the life cycle of the electricity industry using the agent-based simulation method. For this purpose, 5 Agents were mentioned and they were simulated by Anylogic software .Then, four scenarios were investigated with experts' opinions. In the first scenario, it is assumed that the attractiveness of gas power and combined has been reduced and the attractiveness of the other two power has been added, the result of this scenario is the increase in hydroelectric power production, which is not cost effective due to the lack of water resources for the country. In the second scenario, with the arrival of a new technology, the household consumer decreased by 40%, and hydro and steam power with a capacity of 40% were working in the power generation cycle, the result of this scenario is the reduction of fuel consumption and also the shortage of electricity produced relative to the electricity consumption. In the third scenario, it is assumed that the consumption rate of all consumer factors has decreased, which results in the lack of investor factor investment. the fourth scenario assumes that all consumers' consumption is constant, as well as keeping the attractiveness developed by the government steady. As a result, the capacity of electricity generation has increased, but we are still facing power shortages.

Modeling socio-hydrological interactions are one of the essential requirements for water resources management in water-stressed areas. The Ardabil aquifer (Northwestern Iran) is one of the restricted aquifers under intense development activities. The water table is dramatically declining leading the area to an upcoming environmental disaster. To address this problem, a simulation-optimization model has been developed in this study, which simulates groundwater level changes and determines the optimal water exploitation values. The simulation and optimization models have been linked by a new method in MATLAB which provides access to various MODFLOW packages and takes up less memory for iterative runs. The simulation-optimization model has been then linked to an agent-based model, which simulates agents’ behavior and their interactions. For the purpose of this modeling, firstly the key agents and their desirability have been determined. Then, the particle swarm optimization algorithm was used to estimate the agents’ desired groundwater exploitation values. In the next step, the best solution was selected according to the experts' point of view using the compromised programming method. Finally, the agent-based model provided the final exploitation values, taking into account social pressure and management rules (incentives and penalties). The results showed that groundwater demand can be reduced up to 22% in comparison to the initial value. This demand reduction resulted in 90 cm increase in the groundwater level for the entire plain.

Due to the key role and importance of using agent-based modeling and simulation tools in various applications, such as urban management and planning, social networks, financial markets, traffic flows, and crisis management, it is worthwhile to evaluate and quantitatively compare these tools based on a well-defined standard framework. So far, several studies have reviewed and compared agent-based modeling and simulation tools from different aspects. However, none of the studies are quantitatively performed based on a well-known standard framework. To fill this research gap, in this paper, five of the most widely used agent-based modeling and simulation tools including AnyLogic, NetLogo, Repast, GAMA, and MASON were compared and evaluated based on the well-known ISO 25010 standard, regarding the quality in use, by Goal-Question-Metric (GQM) method. The evaluation results show that AnyLogic tools are of better quality than others, and this is due to the attractiveness of the tool from the user's point of view and less effort to develop an agent-based application compared to other tools.

The implementation of government legislation, social responsibility, environmental concerns regarding the reduction of waste, hazardous material and other consumer residuals have made the competition between the firms stricter than ever and nowadays firms that want to survive need a more productive and innovative approach toward the financial aspects of their businesses. This paper presents a conceptual supply chain model integrating financial and physical flows. The main idea of this paper is to identify financial tools and show the usefulness of them in order to increase the competitiveness of the supply chain as a whole in the volatile markets. In doing so, we consider the role of a financial service provider and try to incorporate the effects of supply chain finance concept on the working capital i. e. liquidity of the member entities. So, the main contribution of this research is to address an innovative approach to model financial flows in supply chain and to introduce the financial tools (supply chain finance practices) in the supply chain framework employing Economic Value Added and cash-to-cash cycle as performance measures and finally devise a conceptual agent-based model and show how agent-based modeling can be beneficial in this field.

With increasing competition in global markets, organizations are paying more attention to the life cycle of their products. To achieve this goal, it is necessary to decide on the studies conducted in the product life cycle, and the more factors are considered, the better the result. Therefore, in this research, an attempt has been made to provide a life cycle optimization model for the electricity industry. In this model, consumer, producer, government, capital and technology factors are considered for simulation, with the help of which you try to study the process of electricity generation and build this process according to the amount of consumption and technology, as well as use. Among them is the reduction of carbon dioxide emissions and their effects on the environment. To analyze the results and optimize the model, the Anylogic software was taken, which was implemented after the implementation of the model to optimize the results of the scenario according to the opinions of experts and the final result was obtained. According to the studies performed from the optimization model presented with the actual results in the results between 1390 to 1398 was consistent with a small distance, which indicates a high validity of the model, also to optimize the model to reduce air and reduce Carbon emissions reduced fossil fuel consumption by changing the technology factor, resulting in a significant reduction in air consumption.