agent-based simulation

Uncertainty in food supply in the country has heightened the focus on the supply chain of semi-prepared foods. Like many economic sectors, the food supply chain has not been immune to the impacts of the COVID-19 pandemic. Food supply, influenced by the behaviors of governments and customers during such crises, can exacerbate uncertainties in the supply chain, underlining the need for simulating the behavior of supply chain actors. Accordingly, the aim of this study is to develop an agent-based simulation to analyze the relationships between actors in the supply chain of ready and semi-prepared foods concerning raw material imports and final product exports during the COVID-19 pandemic.

This study is applied in its aim and descriptive-analytical in terms of data collection, utilizing agent-based simulation techniques. The case study for simulating pandemic conditions in the supply chain is one of the ready and semi-prepared food companies in Tehran Province with a production capacity of 216,000 tons per month. To identify factors, variables, and parameters for the agent-based model, the research employed thematic analysis, reviewing prior studies, and conducting interviews with 20 industry experts from the ready and semi-prepared food sector and supply chain experts from Amadeh-Laziz Company.

The findings identified three main actors in the food supply chain that play a role in exports: “Government”  (e.g., customs, national standards organization, consumer protection organizations, industry and mining banks, Iran Chamber of Commerce-Industries-Mines and Agriculture, and the Food and Drug Administration), “Supply Chain Companies” (e.g., domestic suppliers, foreign suppliers, manufacturers, and distributors), and “Final Customers” (e.g., domestic and foreign customers). The key uncertainty identified in interviews was governmental intervention or non-intervention in exports and imports.Accordingly, two scenarios were developed: “Governmental intervention in exports and imports”, and “Lack of governmental support in exports and imports during the pandemic”. These scenarios were analyzed using agent-based simulation to examine the relationships, characteristics, and decision-making behaviors of supply chain actors. Additionally, fuzzy DEMATEL was used as a complementary method to identify the most significant relationships among actors in the ready and semi-prepared food supply chain. Simulation parameters included: Import tariffs on raw materials (%), Export tariffs on final products (%), Total monthly domestic demand (in thousand tons), Total monthly foreign demand (in thousand tons), Total monthly demand (in thousand tons), Maximum production capacity (in thousand tons).Simulation variables included: Percentage change in total costs compared to the base price, Percentage of completed domestic customer orders, Percentage of completed foreign customer orders, Total completed orders (total sales volume), Profits from the domestic market, Profits from the foreign market, Percentage of overall profitability. The simulation was performed under the two scenarios of governmental non-intervention and governmental intervention in importing raw materials and exporting final products, with emphasis on two main variables: profitability and order fulfillment rate, which encapsulate the model’s overall outcomes.

The results indicate that under the governmental intervention scenario, profitability increased by 6%, and total order fulfillment improved by 14%. Based on the defined criteria for scenario comparison, the second scenario (governmental support) provides more favorable conditions for supporting domestic production, enhancing employment levels, and ensuring food security in both domestic and international markets.

This paper has investigated the inventory and financial flows in supply chains. Its purpose is to provide a method to optimize these two flows for chain members, where Return on Capital (ROC) is defined as the dependent variable, and cash conversion cycle (CCC) equation components, which show financial and physical flows, are formulated as independent variables. The data of chain members from six selected industries, including auto & parts, pharmacy, food, petrochemical, metal, and mining, have been extracted. Two scenarios, 1) revision of independent variables without a change in the cash conversion cycle of the entire supply chain, and 2) reducing the days of independent variables along with reducing the cash conversion cycle, have been defined. The problem is simulated using Agent-Based Modeling and NetLego software. Results of the first scenario indicate that if Days Inventory Outstanding (DIO) is reduced in downstream and transferred to upstream of the chain, and Days Payment Outstanding (DPO) in the upstream is shortened, ROC is improved for the entire chain. Also, the results of the second scenario show that, in proportion to the reduction of the cash conversion cycle through productivity under collaboration of chain members, the performance improvement of ROC is remarkable.

One of the economic phenomena that has been investigated in different ways by researchers, is the problem of bank runoff. A bank rush refers to a situation where depositors suddenly withdraw their deposits due to fear of the safety of their deposits, in a mostly emotional reaction, which can lead to bank failure.

In this article, while combining the architecture of Emotional X-machine for the emotional agent  and Norma Emergence(NOE) for the normative agent, a model for the emotional-normative agent is presented and through it, the role of social norms in preventing ​​the bank rush phenomenon through simulation in Netlogo’s environment provided.

In a situation where without the use of normative reasoning, by activating the contagion of emotions and using 15 secondary agents, we have faced 100% bank failures. By activating normative reasoning, considering the mean of 0.5 for agreeableness personality variable and using the initial normative population of 50%, the number of bank failures will decrease to about 50%. By increasing the initial normative population to 100%, bank rush is completely contained.

Finally, the results obtained from the simulation clearly confirm the effectiveness of normative variables in preventing the occurrence of bank runs.

The development of the Internet of Things can be used to empower and improve the quality of human life, especially for the disabled. In this regard, the use of Internet of Things services in the field of rehabilitation requires the new Internet of Things businesses; Therefore, it is necessary to determine competitive and cooperation strategies in completing the product chain for the development and evolution of new businesses. The key issue is how to analyze the components of the business ecosystem and evaluate the role and behavior of actors who interact with each other to achieve co-evolution. This research aims to provide an analytical model for the development of the business ecosystem of the Internet of Things and to analyze the interactions and behaviors of its actors. According to the complexity and uncertainty of the mentioned ecosystem, a model based on the agent-based approach in the technology management system was proposed, and in the following, the simulation and analysis of the said model to network businesses based on the layers of the Internet of Things architecture and actors' interactions in Economic, technological, social and legal environments took place. The findings indicated a change in the behavior of associations, partners, government, and stakeholders in different roles and their influence on each other; Therefore, based on the analysis of the behavior of the agents, it led to the determination of the contribution and role of the actors in the direction of the evolution of the ecosystem. The findings of the present research can help policymakers and executive managers to improve the development strategy of the Internet of Things business to provide the services needed by the disabled.

One of the most important factors in understanding customer behavior is identifying their expectations. Therefore, this study in order to design customer expectations model for platform with agent-based model approach. At first, for finding customers and servants expectations was used of semi-structure interview and then was done automatic clustering with using meta-heuristic algorithms in order to find different factors. Then, with the factor-based simulation approach, the model was designed in Any Logic software. After designing the model, using design method of Taguchi experiments (Qualitek-4 software), scenarios for the growth and development of the platform based on effective factors (liquidity the quality of communication and trust) designed on four levels and finally simulation was performed and scenarios were examined in the simulation environment. the research results showed that the appropriate level of platform growth and development indicators in the fourth level of liquidity, the fourth level of communication quality and the fourth level of trust. In addition, after implementing the optimal scenario in the simulation environment was determined that the percentage of value created on the Instagram platform due to the implementation of the desired scenario is equal to 0.934.