جستجوی مقالات مرتبط با کلیدواژه "agent based modeling" در نشریات گروه "صنایع"

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.

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.

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.

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.

Retailers commonly offer discounts to encourage consumers to purchase more products thereby increasing retailers’ revenues. This article focuses on modeling the seller pricing decisions by using agent-based approach when the price, as a tool of revenue management, decreases. Considering the seller as an agent who uses price changes to maximize its total revenues, the objective of this research is to find the proper seller’s decision about the rate of discount on products in 3 different scenarios. In the first scenario, all products’ price elasticity of demand are the same and the products have relatively elastic demand. In the second scenario, all goods have the same price elasticity of demand and have relatively inelastic demands. The third scenario presents a combination of the first and the second scenarios in which the price elasticity of demand of products are different and goods with elastic and inelastic demand are placed next to each other. Also, all goods in each scenario are substitutes. In the first scenario, reducing the price causes the downward trend in rate of profit even though the discount could increase the revenue. In the second scenario, the agent behaves differently which offering the discount does not increase the revenue. In the third scenario, the products’ discount increases the revenue with a slope less than the first scenario. Also, the discount for all products doesn’t cause income growth. Therefore, some goods without any discount remain in shelf. Consequently, the proposed model in this research shows the proper rate of discount on each product in different product layouts.