Agent-based Approach Research Articles

Agent-based simulation model of multimodal iron ore concentrate transportation

Most global supply chains are implemented through the use of some different types of transport. This especially applies to general cargo: iron ore, oil, grain. As the participants in the transport process increase, the risks of delays, interoperational downtime, and delays in deliveries increase. Therefore, the improvement of multimodal cargo transportation remains an urgent scientific and applied problem. The aim of studying was to research technical and operational parameters of the multimodal supply chain of cargo delivery (on the example of iron ore concentrate). The research's aim of the study was realized by the development of an agent-based simulation model. The simulation model is implemented in the Any Logic Research Edition environment with Java SE, as this toolkit allows combining discrete-event and agent-based approaches in the simulation simultaneously. As a result of the experiment with the developed simulation model, it was found that: 1) approximately 40% of the delivery time is spent waiting for the transport unit to load (8%) and freight being under collecting up to the loading rate into the transport unit (33%); 2) the sensitivity experiment of the model has determined that of all the variable technological parameters of the basic model, the difference in the ratio between the rate of loading of the railway train and the sea vessel has the greatest influence on the average time of freight delivery; 3) the relationship between the capacity of the vessel (with a constant rate of mass shipment into the railway train) and the average time of shipment collecting to the rate of loading into the vessel in case of accidental arrival of freight by rail in the transshipment terminal is highly approximate linear dependence. The results of the research can be used to improve the logistics chains for the delivery of iron ore concentrate from Ukraine to other countries

Evaluating Peer-to-Peer Electricity Markets across the U.S. Using an Agent-Based Modeling Approach

The diffusion of distributed energy resources can overcome some challenges associated with the historical centralized model of electric power distribution. Decentralized generation by residential solar photovoltaic cells creates the potential for peer-to-peer (P2P) electricity trading, where households can act as consumers and prosumers to buy and sell renewable electricity. P2P energy markets are emerging at locations across the globe, and market performance is affected by various social, economic, and environmental factors. This research applies an agent-based modeling (ABM) framework to simulate electricity trades between heterogeneous households in a decentralized market. The P2P system is tested for 15 locations in the United States that vary in climate parameters and local economic factors. The results from these simulations are compared to assess how differences in climate, demand pattern, retail rate, and irradiance affect market performance. Simulations demonstrate that market outcomes rely on the ratio of prosumers to consumers, environmental factors, and geographic conditions. Battery energy storage overcomes limitations associated with faulty forecasting and improves the flexibility of household-generated solar resources to increase the proportion of production that is sold in the P2P market. The application of the agent-based modeling framework demonstrates how P2P markets can be expected to perform for various locations and can be applied to assess alternative locations for market performance.

Multi-Agent Simulation on Staff Evacuation Behavior in Elderly Nursing Home Fire Emergencies

Elderly nursing homes in South Korea are vulnerable to evacuation in emergency situations such as fires. There are many elderly residents with reduced self-walking ability, so if a disaster such as a fire occurs, it is very likely to lead to a large number of human injuries. In elderly nursing homes, it is impossible for many elderly people to evacuate voluntarily without the help of staff. Therefore, it is very important to guide the behavior of staff when evacuating. The purpose of this study is to evaluate the effect of evacuation behavior of staff on evacuation time in a fire emergency in an elderly nursing home by adopting an agent-based simulation approach. The effect of staff evacuation behavior on the evacuation time was investigated based on the results of a new agent-based evacuation simulation model. In the simulation model, there are conditions for caregivers to designate and evacuate the elderly, as well as to set the evacuation priority for the elderly. The results of this study show that having the elderly occupants designated by the staff according to their zones and evacuating them together was the most important in reducing the evacuation time. This study contributes to a new agent-based evacuation simulation model by confirming whether the evacuation behavior of employees affects the evacuation time.

Simulation of Corruption Decisions—An Agent-Based Approach

Corruption is one of the biggest barriers to sustainable development. Several objectives of the Sustainable Development Goals (SDGs) are directly linked with the fight against corruption, as it has an influence on the achievement of every single development goal. The aim of this research is to examine decision-making patterns in a typical corruption situation. The following research questions have been formulated: (1) what kind of decision-making approaches are effective against partners maintaining different strategies, and how do these relate to certain generations? (2) Is there a difference between the behavior patterns of employees of SMEs, large corporations, and the public sector in a situation that provides the opportunity for corruption? To answer these research questions, an agent-based corruption experiment was conducted, building on the prisoner’s dilemma. The relationship between cooperation and corruption was examined through the analysis of decision-making situations to uncover when and with which partners (artificial agents) the participants first start to cooperate or become corrupt. The results show that there is a significant difference in the propensity to cheat among different generations. Furthermore, the behavior patterns of employees of large corporations, SMEs, and the public sector also show deviation in a corruption situation.

Enhancing sustainability within industrial cooperative networks through the evaluation of economically compromised entities

IntroductionWhile promoting sustainable industrial cluster development using the circular economy perspective, the increased use of underutilized materials results in compromised profitability in the cooperative network. The focus is to evaluate the external financial support required in industrial clusters against specific objectives and highlight the potential beneficiaries and losers in financial terms because of industrial symbiosis while closing material loops.MethodThis study provides an agent-based approach to record the system response based on three case studies to demonstrate the potential cost variations: The first case is about utilizing only naturally available resources by all the industrial entities and is taken as a base case. The second case is about targeting the lowest cost for each product, and the third one provides a sustainable and flexible solution by targeting the best transformation methods and materials.ConclusionThe study concludes with valuable insights to identify the economically compromised entities in an industrial cluster network by considering economic deviations beyond a critical value. It can help take concrete measures in the form of incentives or investors subsidies by governmental organizations, regulators, and policymakers to intervene and stimulate markets through targeted financial support/policies to the compromised entities. This results in improved materials loop closing, essentially promoting sustainable production systems in industrial clusters. Additionally, such financial support/incentives also influence the scarcity or accumulation of by-products or low-value materials, ultimately improving the industrial network's environmental and economic performance.

Examining construction group’s safety attitude resilience under major disruptions: An agent-based modelling approach

The construction industry is undergoing a series of disruptions and industrial changes. In disturbances of major events, construction workers’ safety attitude is at risk of being sabotaged due to factors such as resource reallocations, job insecurity, and mental pressure. The extend of such impact on the safety attitude by the disruptive event is complicated and complex as it depends on individual, group and external factors. This research extended and applied successfully the resilience engineering theory to model the response-recovery process of individual workers’ safety attitude under disruptions. With data collected from questionnaire surveys, an agent-based model was built to describe the dynamic changes of safety attitude under disruptions. The model examined the influence of factors at individual, group and external environmental aspects on both response and recovery stages. The results showed that construction workers’ safety attitude was resilient under external disruptions, and group and external level factors determined the length of recovery process, while individual factors determined if the safety attitude could be eventually fully recovered. When the safety parameters decreased by 28%, the construction group’s average safety attitude would not fully recover, unless compensated by the learning adaptability (LA) increasing by 10%. Furthermore, the results revealed that a high level of group interactions (doubled active-ratio) absorbed the external disruptions and the influence on construction workers with high degrees (doubled centre-value) had greater impact on the overall safety attitude resilience. This research shed lights on developing managerial suggestions to cope external disruptions and maintain the positive and stable safety attitude.

As simple as possible but not simpler?: the case of irrigation modeling at catchment scale in southwestern France

The estimation of irrigation water requirements (IWR) amount and timing is crucial for designing water management strategies at the regional scale. Irrigation requirements can be estimated with different types of models: very complex and detailed crop models, agent-based models, or simplified modeling approaches. Because simplified approaches are often preferred, in this study, we evaluate the consequences of using simplified approaches for IWR assessment at a catchment scale and the consequences of various modeling choices, providing information on the uncertainties. To this end, different simple modeling approaches based on the CropWat model are compared with an agent-based approach (MAELIA), which serves as a benchmark. To assess simulations in detail, partial variance is calculated for several indicators characterizing daily simulated irrigation. Our sensitivity analysis, applied over a sub-catchment of the Aveyron River (southwestern France), shows a high variability in simulations produced by CropWat between the modeling assumptions tested, principally explained by the rules for irrigation triggering and the quantification of daily irrigation. The analysis also shows that several simplified approaches are able to reproduce the irrigation simulated by the high-accuracy MAELIA model, but not necessarily corresponding to an optimal irrigation scheme. Hence, this study confirms the possibility of assessing daily irrigation with simplified approaches, but warns about high modeling uncertainties, reflecting uncertainty in effective irrigation practices. This uncertainty can be taken into account by water managers and modelers through the combination of a set of irrigation models.

Modeling social interaction and metapopulation mobility of the COVID-19 pandemic in main cities of highly populated Java Island, Indonesia: An agent-based modeling approach

IntroductionCoronavirus transmission is strongly influenced by human mobilities and interactions within and between different geographical regions. Human mobility within and between cities is motivated by several factors, including employment, cultural-driven, holidays, and daily routines.MethodWe developed a sustained metapopulation (SAMPAN) model, an agent-based model (ABM) for simulating the effect of individual mobility and interaction behavior on the spreading of COVID-19 viruses across main cities on Java Island, Indonesia. The model considers social classes and social mixing affecting the mobility and interaction behavior within a sub-population of a city in the early pandemic. Travelers’ behavior represents the mobility among cities from central cities to other cities and commuting behavior from the surrounding area of each city.ResultsLocal sensitivity analysis using one factor at a time was performed to test the SAMPAN model, and we have identified critical parameters for the model. While validation was carried out for the Jakarta area, we are confident in implementing the model for a larger area with the concept of metapopulation dynamics. We included the area of Bogor, Depok, Bekasi, Bandung, Semarang, Surakarta, Yogyakarta, Surabaya, and Malang cities which have important roles in the COVID-19 pandemic spreading on this island.DiscussionOur SAMPAN model can simulate various waves during the first year of the pandemic caused by various phenomena of large social mobilities and interactions, particularly during religious occasions and long holidays.

Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: An agent-based modeling approach

Rumor is a typical social psychological phenomenon, and forgetting is a universal psychological trait. Exploring the role of forgetting will improve the understanding of rumor spreading and its control. Based on the classical SIR epidemic framework, the conceptual model of rumor spreading and rebuttal strategy is proposed with consideration of forgetting, and the mathematical model at the individual level is constructed from a competitive innovation diffusion perspective. A Java-based Agent-based Modeling approach is adopted to conduct comprehensive simulation experiments. Results indicate that both rebuttal strategy and rebuttal forgetting would affect the process and the result of rumor spreading and control, and complex interactions exist among different factors. Although the influence of control strategy is dominant in most cases, more active control might be not optimal in extreme cases because of the moderation of forgetting, the nonlinearity of influence increases the control challenge.

A coupled agent-based model to analyse human-drought feedbacks for agropastoralists in dryland regions

Drought is a persistent hazard that impacts the environment, people's livelihoods, access to education and food security. Adaptation choices made by people can influence the propagation of this drought hazard. However, few drought models incorporate adaptive behavior and feedbacks between adaptations and drought. In this research, we present a dynamic drought adaptation modeling framework, ADOPT-AP, which combines socio-hydrological and agent-based modeling approaches. This approach is applied to agropastoral communities in dryland regions in Kenya. We couple the spatially explicit hydrological Dryland Water Partitioning (DRYP) model with a behavioral model capable of simulating different bounded rational behavioral theories (ADOPT). The results demonstrate that agropastoralists respond differently to drought due to differences in (perceptions of) their hydrological environment. Downstream communities are impacted more heavily and implement more short-term adaptation measures than upstream communities in the same catchment. Additional drivers of drought adaptation concern socio-economic factors such as wealth and distance to wells. We show that the uptake of drought adaptation influences soil moisture (positively through irrigation) and groundwater (negatively through abstraction) and, thus, the drought propagation through the hydrological cycle.

Simulating the Diffusion of Residential Rooftop Photovoltaic, Battery Storage Systems and Electric Cars in Italy. An Exploratory Study Combining a Discrete Choice and Agent-Based Modelling Approach

Rooftop solar photovoltaic (PV) systems could significantly contribute to renewable energy production and reduce domestic energy costs. In Italy, as in other countries, the current incentives generate a modest annual increase after the generous fiscal incentives that kick-started the PV market in the 2008–2013 period. Several factors are, however, at play that can speed up the installation process, such as the improvements in PV technology at declining prices, the increased availability of battery-storage (BS) systems, the growing use of electric appliances, the uptake of electric cars, and the increased environmental awareness. We integrate two research methodologies, discrete choice modeling and agent-based modeling, to understand how these factors will influence households’ decisions regarding PV and BS installations and how agents interact in their socioeconomic environment. We predict that in Italy, given the preference structure of homeowners, the continuing decline in costs, and the social interaction, 40–45% of homeowners will have PV or PV and BS installed by 2030, thanks to the existing investment tax credit policy.

Unraveling the Most Influential Determinants of Residential Segregation in Jakarta: A Spatial Agent-Based Modeling and Simulation Approach

This study involves the analysis of the residential segregation patterns in Jakarta, Indonesia, one of the largest global metropolitan cities. Our objective is to determine whether similarities in religion or socioeconomic status are more dominant in shaping residential segregation patterns in Jakarta. To do so, we extended Schelling’s segregation agent-based model incorporating the random discrete utility choice approach to simulate the relocation decisions of the inhabitants. Utilizing actual census data from the 2010–2013 time period and the Jakarta GIS map, we simulated the relocation movements of the inhabitants at the subdistrict level. We set the inhabitants’ socioeconomic and religious similarities as the independent variables and the housing constraints as the moderating variable. The segregation parameters of the inhabitants (i.e., dissimilarity and Simpson indexes) and the spatial patterns of residential segregation (i.e., Moran index and segregation maps) were set as the dependent variables. Additionally, we further validated the simulation outcomes for various scenarios and contrasted them with their actual empirical values. This study concludes that religious similarity is more dominant than socioeconomic status similarity in shaping residential segregation patterns in Jakarta.

Цифровая модель прогнозирования трудоустройства выпускника вуза с учетом поведенческих аспектов

Introduction: in modern conditions of labor market adaptation to the new challenges of the digital economy there is a need to search for new analytical tools to assess human behavior in the labor market at different stages of its life cycle. This applies to the applicant’s choice of an educational trajectory (future profession, place of study), as well as the choice of a professional trajectory related or unrelated to the profession received. Objectives: development of a digital model for predicting the employment of a university graduate in the labor market using an agent-based approach. Methods: simulation modeling methods, agent-based approach, system dynamics, analysis, synthesis, sociological research. Results: the proposed model considers the behavior of a job seeker – a university graduate in competition with populations of “Unemployed”, “Migrant” agents applying for vacant positions of agentsenterprises that form the staffing needs of the region. The model was tested in the case of labor market employment of Bashkortostan Republic universities graduates. The forecast data for 2025 have been received on the number of employed university graduates and the number of migrating graduates with no employment; the number of unfilled vacancies, i.e., unsatisfied needs of the labor market has also been determined. Conclusions: the resulting digital model allows to assess the impact of individual state policy measures on changing the professional and qualification imbalance of the market through the assessment of the reactions of specific individuals. Public authorities can use the model to adjust educational or labor policies.

Realization of Large-Scale Social Simulation Models by an Agent-Based Approach

Realization of Large-Scale Social Simulation Models by an Agent-Based Approach

Budget Processes Modeling: Agent-Based Approach

The financial and economic crisis caused by anti-Russian sanctions and geopolitical tensions necessitates an increase in budget expenditures at all levels, in connection with which the issues of improving the fiscal system and reorganizing interbudgetary relations are being updated. To develop plans for adapting to current challenges, achieving the goals of fiscal stability and improving economic performance, transformations in the field of budget processes, the implementation of an effective fiscal policy, and a rethinking of the principles of fiscal federalism are necessary. Achieving this goals predetermines the need to use flexible budget analysis tools, such as agent-based modeling, which allows to reproduce the multi-level structure of the country's economy and budget system, as well as simulate complex budget processes. The article presents an overview of agent-based models developed by foreign and Russian researchers that reproduce various aspects of budgetary processes. The analysis of the architecture of models was carried out and the conceptual ideas of building models in accordance with the types of agents, their behavioral algorithms, as well as the goals of simulation modeling were considered. The expediency and efficiency of using the agent-based approach for modeling complex budgetary processes is substantiated. The analysis carried out in the article allows us to conclude that the agent-based simulation approach is successfully used for the following purposes: testing variable strategies for reforming budgetary processes, predicting the consequences of implementing fiscal policy, choosing the optimal strategy aimed at achieving budgetary self-sufficiency of the regions, as well as creating tools for assessing the effective allocation of budgetary funds to achieve target variables. The results of the study are proposed to be used for agent-based modeling of sanctions effects and search for strategies for reforming the fiscal system in order to counter the financial and economic crisis.

Применение суперкомпьютерных технологий в моделировании развития общества

Under consideration are issues of using supercomputer technologies in the society development modelling. It has been shown that in the TOP 500 most powerful machines in the world, the USA takes the first place (150 systems), the second place – China (34 systems), Russia has 7 systems; ahead of all in performance is the USA (46%), Russia’s share is 1.4%. The supercomputers main tasks have been listed: deep neural networks customization for managing; composite materials development; drugs modelling; calculation of vehicle bodies optimal geometry and etc. The dynamic development of the situation has been noted in Russia, in particular by the increase of the Lomonosov supercomputer power. The МГУ-27 supercomputer has been distinguished, its main tasks: climate, drugs modelling, as well as the Digital Earth superproject. Three priority areas have been identified for implementing on the supercomputers within the framework of BRICS. Russia has announced climate and demography as priorities. In the CEMI RAS, an agent-based model – a Digital Twin of Russia and a Digital Twin of the Planet on the agent-based approach have been developed. The work has been performed jointly with the colleagues from the PRC. Most of the models made in the CEMIRAS have been implemented on the supercomputers. The work results have been published in the «Scientific Solutions of Complex Economic and Social Tasks Using Supercomputers» monography.

Development of a simulation model for evaluating the operating modes of the technological process of product quality control

Currently, one of the priorities of industrial enterprises is to reduce the defects of manufactured products, there- fore, to increase the quality of the products supplied, and therefore increased attention is paid to the areas of control. Hence, one of the most important stages of the product manufacturing process is quality control. The purpose of this work is to evaluate the operating modes and characteristics of the technological process of product control by developing a simulation model. In this paper, a simulation model based on an agent-based approach is proposed for checking product quality control and enterprise resource management. A model and tools for managing the quality control process of products are proposed, which allow determining the optimal amount of resources used, equipment of the enterprise, taking into account the achieved effect of lean manufacturing.

Simulation of Shared Autonomous Vehicles Operations with Relocation Considering External Traffic: Case Study of Brussels

Simulation studies on Shared Autonomous Vehicles (SAVs) could offer several insights to transport policymakers about future mobility. Case studies using the agent-based approach have recently emerged worldwide to test the impacts of SAVs on local networks. However, as the most densely populated area in Belgium, a relevant SAV study for Brussels is currently lacking. In this paper, we have simulated the impacts of substituting Brussels private car trips for people who either only travel in Brussels or do not travel to external places by car. The simulation was carried out with various SAV fleet sizes implementing vehicle relocation strategy. This paper describes how we transferred certain car trips to SAVs, and the respective SAV parameters used for simulation. The results suggest that under a 10% current travel demand, if all current studied drivers groups are willing to carpool as long as the additional travel times do not exceed a certain threshold, 1000 SAVs will be sufficient to replace these car trips in Brussels with the average daily request waiting time of around 5 minutes. By deploying SAVs with the proposed operational scheme, agents’ overall travel time and traffic congestion in Brussels can also be significantly reduced.

Economic and Labour Market Impacts of Migration in Austria: An Agent-Based Modelling Approach

Economic and Labour Market Impacts of Migration in Austria: An Agent-Based Modelling Approach

Simulating the Effects of Law on Patent Licensing Process: Agent-Based Modeling Approach

Simulating the Effects of Law on Patent Licensing Process: Agent-Based Modeling Approach