Agent-based Modeling And Simulation Research Articles

Off-peak hour delivery system: an agent-based modelling and simulation approach

Many cities around the world have experienced an increase in population and pose challenges in planning and managing the freight transportation within the city. Off-peak hour delivery (OPHD) is a strategy of delivering goods in the afternoon and evening to reduce congestion during peak periods, increase the efficiency of delivery companies, and reduce emissions. This research proposed a model of city logistics in the retail supply chain by implementing OPHD in the logistics system. The aim of this study is to explore the extent of potential implementation of OPHD in urban logistics. An agent-based modeling and simulation (ABMS) approach is applied to model the autonomous and dynamic interactions of stakeholders involved in city logistics systems. Preliminary results are presented in this paper to provide illustrations of the model. It shows that OPHD can significantly reduce the transportation cost and emissions cost, and ABMS is able to provide results that are relatively close to pilot test drive research which is carried out in real world.

The protection of the natural environment in armed conflicts and agent-based modelling

Abstract This article seeks to demonstrate the nexus between agent-related technology and the protection of the environment in armed conflicts, looking at how agent-based modelling and simulation (ABMS) can be used as a tool to protect the environment in armed conflicts. It further analyzes the precautionary principle and due regard, as relevant rules, and explains the legal benefits of deploying ABMS to protect and preserve the natural environment. The article argues that the deployment of ABMS helps States to better understand the environmental effects of conflicts, reassess their military activities and comply with the relevant applicable rules and norms.

Agent-Based Modeling and Simulation (ABMS)on the influence of adjusting medical service fees on patients' choice of medical treatment

BackgroundWe explored the impact of medical service fee adjustments on the choice of medical treatment for hypertensive patients in Beijing. We hope to provide decision-making reference to promote the realization of hierarchical diagnosis and treatment in Beijing.MethodsAccording to the framework of modeling simulation research and based on the data of residents and medical institutions in Beijing, we designed three models of residents model, disease model and hospital model respectively. We then constructed a state map of patients’ selection of medical treatment and adjusted the medical service fee to observe outpatient selection behaviors of hypertensive patients at different levels of hospitals and to find the optimal decision-making plan.ResultsThe simulation results show that the adjustment of medical service fees can affect the proportion of patients seeking medical treatment in primary and tertiary hospitals to a certain extent, but has little effect on the proportion of patients receiving medical treatment in secondary hospitals.ConclusionsBeijing can make adjustments of the current medical service fees by reducing fees in primary hospitals and slightly increasing fees in tertiary hospitals, and in this way could increase the number of patients with hypertension in the primary hospitals.

A methodology for selecting a performance-convenient ABMS development framework on HPC platforms

Agent-based modeling and simulation (ABMS) is an approach for simulating the actions and interactions of autonomous agents. Such interactions occur within a defined environment to assess their effects on a system as a whole. Depending on the complexity of the model and the number of simulated agents, an ABMS application may require a significant amount of computational resources. It makes them good candidates to be parallelized on HPC systems. However, most developers of ABMS simulators are experts in the specific simulation domain, but they lack the expertise to develop parallel applications. Consequently, several frameworks for generating HPC ABMS applications have been developed, and it may now be challenging for these non-expert users to choose which of these frameworks would provide the best performing simulator for a particular model. This paper presents a methodology that uses a benchmark to help non-expert users to select the most suitable framework to generate the best performing parallel implementation for a given ABMS model. Such a benchmark considers the common characteristics of parallel ABMS applications and includes parameters for influencing their relevant performance aspects. The methodology is based on defining a set of problem classes that represent the majority of known ABMS models and systematically conducting a series of experiments to determine which framework offers the best performance for each class. Then, users only need to identify the class that closely aligns with their model to make an informed decision regarding the appropriate development framework. The methodology is used to assess well-known ABMS parallel development frameworks (FLAME, RepastHPC, and DMASON) on real HPC platforms. The obtained results are validated using a real application for infection and contact tracing modeling.

New product demand forecasting and production capacity adjustment strategies: Within-product and cross-product word-of-mouth

Manufacturers attach increasing importance to consumer word-of-mouth (WOM) as it assists them to improve demand forecasting and determine production capacity adjustment strategies. Communication among potential consumers occurs as a result of within-product and cross-product word-of-mouth. Within-product WOM has received due academic attention touching upon production volume adjustment under infinite capacity, while the impact of cross-product WOM on demand is under-researched with limited number of studies investigating production capacity adjustment. To address this research gap, we employ Agent-based Modeling and Simulation (ABMS) approach and the Bass model, together with considering within-product WOM and cross-product WOM, to modify previous demand forecasting methods and propose production capacity adjustment strategies. Results show: (1) higher performances delivered by manufactures depends more on production capacity adjustment strategies rather than production volume adjustment strategies; (2) production capacity adjustment strategies are closely associated with factors including unit capacity fixed cost and cross-product WOM; (3) on condition of low unit capacity fixed cost, proactive strategy leads to maximal expected net present value (NPV) of profit and minimal lost customer number (LCN); (4) greater within-product WOM and cross-product WOM might not as beneficial as expected, in that expanding within-product WOM and cross-product WOM in most cases leads to growing lost customer number.

Agentenbasierte Modellierung und Simulation im Pandemiemanagement

Mathematical models and computer simulations play a crucial role in the context of the COVID-19 crisis for knowledge about the possible course of the pandemic and for appropriate policy decisions. The paper presents results from an ethnographic study of a government-funded R & D project dealing with agent-based modeling and simulation (ABMS) in the context of pandemic management. Based on the assumption that the use of computer simulations in pandemic management is not only a means to an end for political or epidemiological goals but also plays a significant role in determining which goals and strategies appear politically legitimate, the paper reconstructs how insights into the pandemic are generated in ABMS and specifically in the researched project and made accessible for decision-making.

МОДЕЛІ ВИЯВЛЕННЯ ТА ПРОГНОЗУВАННЯ ДИНАМІКИ ІНФОРМАЦІЙНИХ ОПЕРАЦІЙ У СОЦІАЛЬНИХ МЕРЕЖАХ

The article examines some provisions for conducting information operations as one of the main threats to national secu-rity in the information sphere, demonstrates the use of social networks as the main field for conducting them thanks to the latest technologies, and analyzes the proposed models of information operations in social networks.The article is devoted to the problem of the complex application of theoretical models used in the study of "viral" infor-mation dissemination in social networks of cyberspace.Formal modeling, which is used to study the peculiarities of communication in social networks, is analyzed.Three components of formal models of online message distribution in social networks are distinguished: typology of messages, network model, formal model of signal distribution.Special attention is paid to the possibilities of applying non-linear models of information dissemination.It is proposed to use a relatively new type of simulation modeling - agent-based modeling and simulation (ABMS) for modeling information operations in social networks. It is agent modeling that allows you to simulate those social processes and user behavior that occur in these new media. The main feature of agent modeling is building scenarios of possible options for the development of events in the future, on the basis of which strategic alternatives, which work in each scenario and serve as a basis for making decisions about choosing an integrated strategy are formed and then selected. The agent approach allows for multivariate situational analysis of the modeled system. The essence of the agent approach in building scenarios consistsin building an environment of active agents, determining the algorithms of their functioning and interaction, identifying new patterns, connections, cognitive connections, as well as a set of mathematical models for the formation of scenarios on a computer simulation complex. The types of virtual agents in social networks are distinguished: primary source, bot accounts, troll accounts, real people.

Analyzing waste prevention behaviors by applying an ABMS framework

Nature and society have undergone fast and intense changes in the last decades. Consumption is expanding at a hyperbolic rate. Technological innovation has bypassed some environmental problems, but it is hardly sufficient to solve them. As a result, understanding the factors related to people's behavior is imperative when finding a novel approach for intervention policies that can effectively alleviate environmental impacts caused by human activities. A promising alternative to designing waste prevention (WP) policies is to develop strategies to endure behavioral change through collective actions. This paper briefly reviews some WP status worldwide and highlights the possibility of using agent-based modeling and simulation (ABMS) to plan WP policies and programs. ABMS enables a more in-depth analysis since experiments with a large sample in real situations are financial, temporal, and social cost-demanding. Preliminary results show an influence of the social norm on the adoption of reusable bags by individuals with medium and lower pro-environmental motivations. Understanding these dynamics relations in which WP policy is embedded makes it possible to forecast future waste generation and composition scenarios. Also, a framework for planning WP with ABMS is proposed.

Emergent behaviours in multi-agent systems with Evolutionary Game Theory

The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and simulation (ABM) techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters, and alternative virtual games. This paper summarises some main research directions and challenges tackled in our group, using methods from EGT and ABM. These range from the introduction of cognitive and emotional mechanisms into agents’ implementation in an evolving MAS, to the cost-efficient interference for promoting prosocial behaviours in complex networks, to the regulation and governance of AI safety development ecology, and to the equilibrium analysis of random evolutionary multi-player games. This brief aims to sensitize the reader to EGT based issues, results and prospects, which are accruing in importance for the modeling of minds with machines and the engineering of prosocial behaviours in dynamical MAS, with impact on our understanding of the emergence and stability of collective behaviours. In all cases, important open problems in MAS research as viewed or prioritised by the group are described.

Surrogate-assisted strategies: the parameterisation of an infectious disease agent-based model

Parameter calibration is a significant challenge in agent-based modelling and simulation (ABMS). An agent-based model’s (ABM) complexity grows as the number of parameters required to be calibrated increases. This parameter expansion leads to the ABMS equivalent of the "curse of dimensionality". In particular, infeasible computational requirements searching an infinite parameter space. We propose a more comprehensive and adaptive ABMS Framework. The new framework can effectively swap out parameterisation strategies and surrogate models to parameterise an infectious disease ABM. This framework allows us to evaluate different strategy-surrogate combinations’ performance in accuracy and efficiency (speedup). We show that we achieve better than parity in accuracy across the surrogate-assisted sampling strategies and the baselines. Also, we identify that the Metric Stochastic Response Surface strategy combined with the Support Vector Machine surrogate is the best overall in obtaining the actual synthetic parameters. Additionally, we show that DYnamic COOrdindate Search Using Response Surface Models with XGBoost as a surrogate attains one of the highest probabilities at 0.75 of approximating the cumulative synthetic daily infection data achieving a significant speedup of 2.9 with regards to our analysis. Lastly, we show in a real-world setting that DYCORS XGBoost and MSRS SVM can approximate the real-world cumulative daily infection distribution with 97.12% and 96.75% similarity, respectively.

Agent-Based Approach on Water Resources Management: A Modified Systematic Review

Water resources management is one of the essential subjects of water research. As a conjunctive term, water resources management denotes all applications of planning, development, distribution, and management of water resources sustainably. Its social, economic, and ecological dimensions make water management a highly complex domain related to many intertwined human-nature systems. Therefore, the decision and implementation of sustainable policies require following the evidence-based approach. Agent-Based Modelling and Simulation (ABM) is one of the latest computer-aided modeling and simulation applications widely used to understand the phenomena associated with water-related/human-oriented engineering systems. In this study, conducting a modified systematic review approach, a field-specific review of the 128 articles on water resources management with ABM methodology was presented. Application areas of ABM in water resources management and examples of its use as a decision support tool were evaluated. As an integrative systematic review of Web of Science, Science Direct, and Google Scholar, this study summarizes the leading work of ABM applications on water resources management. Current trends show that water research professionals have often used ABM as a social simulation tool. Due to its role in facilitating interdisciplinary research, its application area is widening. However, there is a need for a comprehensible and open share of application-oriented information to guide the scientific community.

A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model

Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault Trees (DFT). The proposed model is called Generic Dynamic Agent-Based Model (GDABM) for risk analysis. GDABM is built on top of the well-known Agent-Based Modeling and Simulation (ABMS) technique. GDABM can model the dynamic system agents in both nominal (failure-free) and degraded (failure) modes. GDABM shows the propagation of failure between system elements and provides complete information about the system’s configurations. In this paper, a complete detailed case study is provided to show the GDABM capabilities to model and study the risk analysis for such dynamic systems. In the case study, the GDABM models the risk analysis for a chemical reactor/operator and performs a complete risk analysis for the entire system. The GDABM managed to simulate the dynamic behavior of the system’s components successfully using Repast Simphony 2.0. Detailed agent behavioral modes and failure modes are provided with various scenarios, including different time stamps. The proposed GDABM is compared to a reference model. The reference model is referred to as the ABM model. GDABM has given very promising results. A comparison study was performed on three performance measures. The performance measures used are (1) Accuracy, (2) response time, and (3) execution time. GDABM has outperformed the reference model by 15% in terms of accuracy and by 27% in terms of response time. GDABM incurs a slightly higher execution time (13%) when compared to the ABM reference model. It can be concluded that GDABM can deliver accepted performance in terms of accuracy and response time without incurring much processing overhead.

Agent-based modeling and simulation of pandemic propagation in a school environment

Spatial behavior is a principal aspect for architects to consider. Unfortunately, it is sometimes hard to predict, as users of space seldom follow a standard routine. However, predicting user behavior in a space can potentially illuminate the entire design process. Moreover, it is a key factor in fighting the recent COVID-19 pandemic. Until most of the public gets their vaccine, health officials recommend social distancing as the primary means of fighting the pandemic. This paper uses an agent-based modeling and simulation (ABMS) approach to reproduce, analyze, and predict spatial behavior during the pandemic in a school environment and its impact on disease propagation. The simulation reproduces the behavior of teachers and students at a sample elementary school. By manipulating parameters that simulate the school officials' response to the pandemic, stakeholders can assess the most effective preventive measures. As a result of running multiple scenarios, the study demonstrates how varying starting conditions and alternate spatial behaviors could lead to different results, providing insight to stakeholders on how to handle disease spread in school environments.

Simulating Socio-Technical Transitions of Photovoltaics Using Empirically Based Hybrid Simulation-Optimization Approach

Energy transitions as socio-technical processes involves interactions among different actors such as households, firms, and government, thus requiring an integrated approach to explore the transition’s dynamics. The present study aims to simulate the socio-technical transitions of photovoltaics (PV) in Indonesia using an empirically based hybrid simulation-optimization model. The model involves households’ decision-making, PV supply chain, and government interventions. The hybrid simulation-optimization model consists of integer linear programming to optimize PV’s supply chain configuration which was embedded within agent-based modeling and simulation (ABM). The empirical data involving 413 households from 34 provinces in Indonesia was acquired from a survey that was specifically designed based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to specify and parameterize the model. Export tariff regulation, incentives for PV investment, environmental campaigns, and the combinations of those interventions were evaluated. The findings demonstrate that all of the interventions increase the intention toward PV, but the intention is not necessarily translated into adoption due to either financial or facility constraints. The findings highlight the necessity to include both demand and supply aspects endogenously in the transition model. The export tariffs combined with the incentives, followed by the export tariffs combined with the campaigns, is found to be preferable due to low supply chain unit cost and high reduction of greenhouse gas. Managerial implications and future research are discussed.

High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation

This paper presents an approach for the modeling and the simulation of the spreading of COVID-19 based on agent-based modeling and simulation (ABMS). Our goal is not only to support large-scale simulations but also to increase the simulation resolution. Moreover, we do not assume an underlying network of contacts, and the person-to-person contacts responsible for the spreading are modeled as a function of the geographical distance among the individuals. In particular, we defined a commuting mechanism combining radiation-based and gravity-based models and we exploited the commuting properties at different resolution levels (municipalities and provinces). Finally, we exploited the high-performance computing (HPC) facilities to simulate millions of concurrent agents, each mapping the individual’s behavior. To do such simulations, we developed a spreading simulator and validated it through the simulation of the spreading in two of the most populated Italian regions: Lombardy and Emilia-Romagna. Our main achievement consists of the effective modeling of 10 million of concurrent agents, each one mapping an individual behavior with a high-resolution in terms of social contacts, mobility and contribution to the virus spreading. Moreover, we analyzed the forecasting ability of our framework to predict the number of infections being initialized with only a few days of real data. We validated our model with the statistical data coming from the serological analysis conducted in Lombardy, and our model makes a smaller error than other state of the art models with a final root mean squared error equal to 56,009 simulating the entire first pandemic wave in spring 2020. On the other hand, for the Emilia-Romagna region, we simulated the second pandemic wave during autumn 2020, and we reached a final RMSE equal to 10,730.11.

A generic representation of supply network resilience using simulation based experimentation

ABSTRACT This paper presents a generic representation of supply network resilience implemented using Agent-Based Modelling and Simulation (ABMS) and simulation experiments. The representation captures the relationships between tactical and strategic decisions and their impact on the performance of multi-echelon networks under supply uncertainty. This representation expands practitioners’ understanding of the behaviour of their supply chains by using the Supply Chain Operations Reference model processes and performance metrics and developing a simulation model that can be adapted to a firm’s unique supply structure (sourcing strategy) and its product’s technological assembly order (Bill of Materials). To demonstrate the applicability and usability of the proposed representation, a case with single and dual sourcing network structures was analysed under disruptions of operational and strategic nature. The example demonstrates the flexibility and functionality of the proposed representation to understand the performance and resilience of multi-echelon networks under a wide range of disruptive scenarios.

An integrative agent-based vertical evacuation risk assessment model for near-field tsunami hazards.

This study couples FN-curves with Agent-based Modeling and Simulation (ABMS) to assess risk for tsunamis with various recurrence intervals . By considering both expected number of casualties and the likelihood of tsunami events, multiple series of simulations and in-depth analyses determine (1) how vertical evacuation structure (VES) placement impacts mortality rate; (2) what the best evacuation strategies VES locations are; and (3) where evacuees are likely to be caught by tsunami waves. The results from utilizing FN-curves to conduct disaggregative analyses based on six tsunami scenarios indicate that choosing one tsunami scenario or averaging the risk of different scenarios may not fully articulate VES impacts due to the "levee effect," which potentially leads to false positives. Findings show that placing VESs close to shorelines saves nearby at-risk populations, but also results in two risk increasing phenomena: "exposure to risk" (i.e., evacuees being attracted to high risk roads by a VES when evacuating) and "blind zones" (i.e., locations near a VES where evacuees increase their risk by evacuating to that VES). When limited to one VES, placement near a population's centroid results in the lowest mortality rate. More than one VES may lower mortality rate further if VESs are spreading out according to community's topography. In addition to the analysis of tsunamis, the approach of coupling FN-curves with ABMS can be used by local authorities and engineers to determine tailored hard-adaptive measures and evacuation strategies, which helps to avoid maladaptive actions in different hazardousevents.

Institutional modelling: Adding social backbone to agent-based models

Institutional modelling is a branch of agent-based modelling and simulation (ABMS) that pays special attention to the social structure by incorporating institutions into these models. Institutions, in this regard, are the rules of the system that shape individual behaviour and interaction. Institutional modelling can make use of the Institutional Grammar (IG), which has 6 pre-specified components to conceptualize institutions. The IG can be used for collecting institutional data for modelling purposes, for coding institutions, and for integrating them into different parts of an agent-based model.This modelling approach helps capture the structural complexities of social systems in agent-based models. It also supports the explanation of the emergence and dynamics of institutions, not only for better understanding institutions but also for studying social systems, especially for policy analysis.•Institutional modelling is a branch of agent-based modelling that focuses on and explicitly models the social aspects of socio-ecological-technical systems.•Institutional modelling supports theory development by enabling modellers to study institutions, and institutional change within, the systems they are embedded in.•While agent-based modelling is a bottom-up (individual-based) simulation approach, institutional modelling also incorporates top-down institutional structures and aims at studying interactions between bottom-up processes and top-down structural patterns.

Agent-Based Simulation for Evaluating the Effect of Different Walking and Driving Speed on Disaster Evacuation in Aceh

Agent-Based Modeling and Simulation (ABMS) was implemented to build and develop an evacuation simulation model. In this research, ABMS is simulated in several evacuation scenarios with an output: the evacuation rate of two different decision choice evacuation modes (walking or driving to the evacuation points). The result of this tsunami evacuation simulation shows that the decision choices on the evacuation mode are highly correlated to the evacuation rate. Observed in the simulation that there is a typical choice that leads to the higher evacuation rate, the choice is by maximizing the pedestrian agents on the population distribution.

An Approach to the Modeling and Simulation of Intra-Hospital Diseases

This publication presents an approach to a simulator to recreate a large number of scenarios and to make agile decisions in the planning of a real emergency room system. A modeling and simulation focused on the point prevalence of intrahospital infections in an emergency room and how it is affected by different factors related to hospital management. To carry out the simulator modeling, the Agent-based Modeling and Simulation (ABMS) paradigm was used. Thus, different intervening agents in the emergency room environment — patients and doctors, among others— were classified. The user belonging to the health system has different data to configure the simulation, such as the number of patients, the number of available beds, etc.
 Based on the tests carried out and the measurements obtained, it is concluded that the disease propagation model relative to the time and contact area of the patients has greater precision than the purely statistical model of the intensive care unit.