agent based modeling

Blockchain is a technology that can be used in various organizations. Blockchain, through a decentralized computer network, leads to the facilitation of high-level transactions of organizations as well as their registration, in order to better respond to people's needs. In this research, a preliminary conceptual model consisting of the behavioral factors of blockchain technology acceptance in the banking industry, which is derived from theoretical literature and research background, is presented. Behavioral factors include facilitating conditions, attitude, literacy and skill, perceived risk, technology, perceived behavioral control, external motivation, internal motivation, competition and mental norm. Then, in order to check the fit of the model, structural equation modeling and smart pls software were used using a researcher-made questionnaire extracted from the research model. For this purpose, due to the unlimited size of the statistical population, 384 samples were randomly selected and the questionnaire was distributed among them. The result indicates that all the relationships are significant and the factors cause more than 80% of changes in technology adoption. In addition, agent-based modeling and Anylogic software have been used in order to predict changes in the adoption of blockchain technology over time, affected by the identified behavioral factors. The results showed that with the improvement of behavioral factors, the adoption of blockchain technology increases over time. In this study, insight is generated for key decision makers and relevant policy makers to propagate the adoption of blockchain technology in the banking industry.

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 new challenge for business managers is to model and simulate an efficient and effective perishable foods supply chain network that is resilient enough to deal with different disruptions. Therefore, this research aims to model a resilient supply chain for unnecessary perishable foods using an agent-based simulation to deal with future disruptions. To confirm the strategies and model, the statistical population and sample include 7 prominent university professors and 11 managers of various departments of companies producing perishable foods (sales department; production department; planning and warehouse department; laboratory and quality control department; and commercial department). NetLogo software has been utilized to test the agent-based model. The simulation environment in this study includes the behavior and interactions between the members of the supply chain of unnecessary perishable foods and the consumers in Shiraz City. The simulation results indicate that the use of strategies such as consumer behavior tracking, discount, awareness of product safety, robotics, the use of blockchain among the levels of distributors and retailers, and the activation of several supporting suppliers, leads to a resilience supply chain of unnecessary perishable foods under different disruptions. In addition, among the different scenarios, the 30% discount and 40% robotics have been the most effective in the resilience of the supply chain of unnecessary perishable foods under different disruptions.

Complexity is seen as a major challenge in supply chain research because of interactions, interdependencies, and uncertainties. The main purpose of this study is to develop agent-based models and simulations that focus on Steel Supply Chain Resilience (SSCR) based on complex adaptive network analysis and graph theory. Before and after the simulation, and by mapping the Iranian SSC to the network in the Gephi environment (0.9.2), we used graph theory to analyze node-level and network-level indices. This hypothesis was tested that the corresponding network of the target SSC contained a Complex Adaptive System (CAS). Agent Based Modeling (ABM) has been proposed as a way to track Iran's steel industry supply chain behavior during the crisis using NetLogo (6.2.0). BehaviorSpace, as NetLogo's integrated software tool, was selected for the proposed parameter sweep, design, and experiment execution of agent-based modeling. For sensitivity analysis, the output files were taken from two types of spreadsheets and six scenarios in the table for XLSTAT statistical analysis.

Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems.

The primary goal of this study is to design an agent-based model of the supply chain for perishable goods during the occurrence of specific disruptions. This study is practical in terms of aim and qualitative in terms of data collection method. To validate the model, the views of the statistical population including prominent university professors and manufacturers of perishable goods and experts with experience and expertise in the area of specific disruptions of the perishable goods supply chain were used. Additionally, the snowball method was used to select the sample. In total, the views of 18 experts were used. Agent-based modeling was done using NetLogo software. In this modeling, all supply chain disruptions of perishable goods such as disruptions at the macro level (change in consumer behavior), demand, production, supply, transportation, information, and Financial were considered. Also, according to each disruption, strategies to mitigate the effects such as blockchain, robotics, etc. were determined. The results of agent-based modeling show that the simultaneous use of different strategies in the perishable goods supply chain during the occurrence of specific disruptions significantly reduces the effects of specific disruptions on the perishable goods supply chain. Vaccination along with the application of other strategies such as the use of blockchain, robotics, discounts, subsidy, online purchase methods, non-cash payment methods, awareness of product safety, green packaging, and employee safety and health have significantly reduced the effects of specific disruptions on the perishable non-necessary goods supply chain. In addition, according to the findings of the research, among the various strategies, the discount has played the most significant role in reducing the influences of specific disruptions on the supply chain of non-necessary perishable goods.

From economic, environmental and social perspectives, the sustainability of the supply chain can give a competitive advantage to organizations. By designing a hybrid discrete event agent-based simulation model based on the simulation-optimization approach and meta-heuristic algorithms, this study has sought to evaluate the sustainability of the supply chain and improve the economic, environmental and social objectives of the supply chain.

First, by identifying supply chain agents, an agent-based simulation model is developed. After designing the hybrid simulation model, the verification and validation phases are performed. By combining the simulation model with meta-heuristic algorithms and using the simulation-optimization approach, the optimal/near-optimal values of the components affecting the sustainability of the supply chain are finally extracted.

In addition to being able to reflect all the complexities of supply chains, the hybrid simulation optimization approach can also improve the key components affecting the sustainability of the supply chain.

Implementation of sustainable supply chain components without optimizing the key variables of the supply chain can lead to the deterioration of performance and sustainability of the supply chain. The components of the maximum levels of product and inventory maintenance and how to implement environmental and social aspects in all the elements of the supply chain have a direct effect on the chain performance and should have appropriate values in different scenarios.

Simulation-based techniques have become increasingly vital in engineering management, offering sophisticated tools for improving decision-making processes across a range of applications. This narrative review provides a comprehensive examination of key simulation methods, including Monte Carlo simulations, discrete-event simulations, system dynamics, and agent-based modeling, and their relevance in addressing complex engineering management challenges. The review highlights how these techniques enhance decision-making by enabling the modeling and analysis of complex systems, thereby allowing managers to predict outcomes, optimize processes, and mitigate risks. However, the implementation of these techniques is not without challenges. Technical difficulties, such as computational complexity and data accuracy, along with organizational barriers, including resistance to change and a lack of expertise, present significant obstacles. Additionally, the current research landscape reveals gaps in the practical application and scaling of these models, underscoring the need for further investigation. The review also explores emerging trends, such as AI-driven simulations and real-time decision support systems, which are set to shape the future of engineering management. Practical recommendations for engineering managers are provided, emphasizing the importance of integrating simulation tools with existing systems and fostering a culture of experimentation and iterative learning. The article concludes by discussing the broader implications of simulation-based techniques for engineering management and the critical need for ongoing research and development in this evolving field.

Agent-based modeling is helpful in simulating reality to predict the unknowns. Evacuation simulation can be effective in preventing undesirable events on actual occasions. This study aims to simulate classroom evacuation with different furnishings in two different conditions: open and closed doors. This model is developed with AnyLogic software. According to the results, the best furnishing in the classroom between a central pathway, central and sides pathways, and separated chairs are related to the second furnishing.  In first furnishing, the brief delay makes people bet to master the conditions. While in other furnishings, the results show the opposite. However, it should be noted that an increase in delay time leads to an increase in reaction time.

Because of the dissemination of Impulse Buying (IB) behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in IB to be taken into account by the researchers and managers of the stores. The purpose of this paper is to model agent-based the IB behavior of consumers (customers), with regards to the factors of discount and swarm in the purchase. In terms of executive purpose and with Agent-Based Modeling (ABM) approach, the present paper examines the existing reality of consumer IB behavior. This paper develops consumption models, examines and analyzes Consumer Behavior (CB) under the NetLogo software environment. In comparing the optimal points of discounts and sales volume in both discount and swarm-discount functions that lead the stores to maximize profits and sales volume simultaneously, it can be debated that with running this model (swarm-discount) stores would be gaining more sales by less discounts. Results could describe customer behavior by implementing discount and swarm factors. Understanding the customer behavior prepared the comparing possibility of customer behavior in store in each introduced mathematical model. The contributions could be considered in two points of view. On the applicable view, this research can provide the managers and decision makers with significant information, includes possibility of forecasting sales volume and incomes of any policies in stores, so the comparing of policies and strategies analysis would be possible. This method is rather less expensive, because of virtual environment nature. Users of this model can study other sections by changing the research assumptions.

The Air Traffic Management system is a complex issue that faces factors such as Aircraft Crash Prevention, air traffic controllers pressure, unpredictable weather conditions, flight emergency situations, airplane hijacking, and the need for autonomy on the fly. agent-based software engineering is a new aspect in software engineering that can provide autonomy. agent-based systems have some properties such: cooperation of agents with each other in order to meet their goals, autonomy in function, learning and Reliability that can be used for air traffic management systems. In this paper, we first study the agent-based software engineering and its methodologies, and then design a agent-based software model for air traffic management. The proposed model has five modules .this model is designed for aircraft ,air traffic control and navigations aids factors based on the Belief-Desire-Intention (BDI) architecture. The agent-based system was designed using the agent-tool under the multi-agent system engineering (MaSE) methodology, which was eventually developed by the agent-ATC toolkit. In this model, we consider agents for special occasions such as emergency flights’ and hijacking airplanes in airport air traffic management areas which is why the accuracy of the work increased. It also made the flight’s sequence arrangement in take-off and landing faster, which indicates a relative improvement in the parameters of the air traffic management

Spatial analysis and distribution are of great importance to transportation planners, especially in traffic demand management. Simulation is an important tool in the planning and management of transportation systems to achieve an estimation of real system behavior to evaluate different scenarios. Regarding the aggregate nature and inability to consider heterogeneity among the individuals in a large number of discrete choice models and the high cost of data collection through questionnaires, using a disaggregate and heterogeneous agent approach can be used to evaluate different policies. Since each agent is inherently autonomous and interacts with different agents and the environment to achieve its goals, this paper aims to use the agent-based approach to simulate the destination choice of discretionary tours of Qazvin citizens. Individual socioeconomic characteristics and travel information questionnaires (revealed preference) of 9938 households and 29840 individuals in 12 municipality districts of Qazvin were collected. After extracting 12 types of activity patterns including shopping and recreation trips, the simulation of destination choice in MATLAB has been studied using the Reinforcement Learning algorithm (RL) and reward-punishment functions which are based on the relative attractiveness of districts for various modes and travel times. High correlation (above 0.9) results were achieved among simulated trip destination choice distributions and observed survey data using the RL algorithm which illustrates the algorithm's goodness of fit; also the simulation results and survey data have a similar trend among districts which illustrates that the simulation findings have real-world implications.

During the last decade, many researchers have been attracted to study the role of uncertainties in their supply chain designs. Two important uncertainties of a supply chain are demand uncertainty and supply disruption. The basic concept of the proposed model of this paper is based on the newsvendor problem. The model consists of many retailers and many suppliers as two types of autonomous agents that interact with each other considering demand and supply uncertainties. To cope with the uncertainties, retailers have three choices: a forward contract, an option contract, and purchasing from the spot market. Retailers maybe risk sensitive or risk neutral. A new simulation optimization approach is developed to find the best behavior of a risk sensitive retailer in contrast with the other risk neutral retailers during the multiple contract periods. In this model two objectives are defined to find the best behavior of the risk sensitive retailer: the maximization of the profit and the service level. In order to optimize the agent based simulation, an NSGA-II approach is used. The proposed simulation based NSGA-II is further developed in two directions: the one is different realization numbers of the uncertain parameters, and the other is preference points. Under the different preference points and different number of realizations, Pareto optimal solutions are discovered by the collaboration of the agents. Results of the numerical studies showed that adopting more risk averse policies during the contract periods will result in a larger service level and smaller profit rather than adopting more risk taking policies.

In recent decades, researchers are turning to the potential of ABMs to study complex phenomena. Due to intrinsic interconnections, structural interactions and inter-dependencies, individual variations, and communications of various components, supply chain network should be accordingly treated as a complex adaptive system. ABM is dominant tool exploring the emergent behavior of supply chain network with numerous interactive agents. This paper aims to conduct a systematic literature review on the agent-based modeling in the concepts of supply chain and various fields of research. Using reputable databases, combining intended keywords and applying filters based on restrictions and indicators, a total of 123 relevant articles are selected from the valid journals and conferences in year 2010-2019, and 17 subjects in association with supply chain management and 23 subjects related to other fields are presented. Moreover, a brief history and the definition of the three basic areas including complex systems, complex adaptive system and agent-based modeling are provided. The main objective is to provide a perspective based on agent-based modeling and complex adaptive systems for researchers in different sciences and especially supply chain researchers, who are not sufficiently familiar with the philosophy and applications of these approaches.