Agent-based Simulation Model Research Articles

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

The development and popularization of new energy vehicles have become a global consensus. The shortage and unreasonable layout of electric vehicle charging infrastructure (EVCI) have severely restricted the development of electric vehicles. In the literature, many methods can be used to optimize the layout of charging stations (CSs) for producing good layout designs. However, more realistic evaluation and validation should be used to assess and validate these layout options. This study suggested an agent-based simulation (ABS) model to evaluate the layout designs of EVCI and simulate the driving and charging behaviors of electric taxis (ETs). In the case study of Shenzhen, China, geographical positioning system (GPS) trajectory data were used to extract the temporal and spatial patterns of ETs, which were then used to calibrate and validate the actions of ETs in the simulation. The ABS model was developed in a geographic information system (GIS) context of an urban road network with traveling speeds of 24 h to account for the effects of traffic conditions. After the high-resolution simulation, evaluation results of the performance of EVCI and the behaviors of ETs can be provided in detail and in summary. Sensitivity analysis demonstrates the accuracy of simulation implementation and aids in understanding the effect of model parameters on system performance. Maximizing the time satisfaction of ET users and reducing the workload variance of EVCI were the two goals of a multiobjective layout optimization technique based on the Pareto frontier. The location plans for the new CS based on Pareto analysis can significantly enhance both metrics through simulation evaluation.

Agent-based cloud simulation model for resource management

Driven by the successful service model and growing demand, cloud computing has evolved from a moderate-sized data center consisting of homogeneous resources to a heterogeneous hyper-scale computing ecosystem. This evolution has made the modern cloud environment increasingly complex. Large-scale empirical studies of essential concepts such as resource allocation, virtual machine migration, and operational cost reduction have typically been conducted using simulations. This paper presents an agent-based cloud simulation model for resource management. The focus is on how service placement strategies, service migration, and server consolidation affect the overall performance of homogeneous and heterogeneous clouds, in terms of energy consumption, resource utilization, and violation of service-level agreements. The main cloud elements are modeled as autonomous agents whose properties are encapsulated. The complex relationships between components are realized through asynchronous agent-to-agent interactions. Operating states and statistics are displayed in real time. In the evaluation, the efficiency of the simulator is studied empirically. The performance of various resource management algorithms is assessed using statistical methods, and the accuracy of server energy consumption models is examined. The results show that agent-based models can accurately reflect cloud status at a fine-grained level.

What Matters in the Effectiveness of Airborne Collision Avoidance Systems? Monte Carlo Simulation of Uncertainties for TCAS II and ACAS Xa

TCAS II is a rule-based airborne collision avoidance system (ACAS) that is used in current commercial air transport operations, and ACAS Xa is a new optimization-based system. Operational validation studies have mainly used deterministic simulations of ACAS performance using various sets of encounters. Recently a new approach was developed, which employs Monte Carlo (MC) simulation of agent-based models to evaluate the impact of sensor errors and pilot response variability. This paper contrasts the results of both approaches in a comparison of TCAS II and ACAS Xa for various types of synthetic encounters. It was found that conventional estimates of near mid-air collision (NMAC) probabilities are often lower than the estimates achieved using MC simulation, and that the biases in the P(NMAC) estimates are consistently larger for ACAS Xa than for TCAS II. Contributions to unresolved risk are largest for pilot performance, then for encounter types, and finally for sensor errors. The contribution of non-responding pilots is much larger than the differences between TCAS II and ACAS Xa. It is concluded that the agent-based MC simulation overcomes the limitations in traditional evaluation of altimetry errors and pilot response, providing an independent means to effectively analyze the robustness of ACASs.

Comparative analysis of the performance of humanitarian logistic structures using agent-based simulation

The post-disaster humanitarian logistic structure (PD-HL) used in a disaster determines the performance of the relief distribution, which impacts human suffering and loss of lives. This paper develops an Agent-Based simulation model to explore the performance of three different PD-HL structures: Agency Centric Efforts (ACEs), Partially Integrated Efforts (PIEs), and Collaborative Aid Networks (CANs). Using the proposed simulation framework, the authors analyze the role of information sharing and crowd movement in the distribution of relief supplies, and evaluate the ability of different PD-HL structures to respond to disasters of varying intensities. The performance of each PD-HL structure is measured using the social costs, i.e., the sum of logistic costs incurred by the relief organizations carrying out logistical activities, and the deprivation costs and walking costs of affected people. The study also discusses the humanitarian operations during the 2010 Port-au-Prince earthquake to demonstrate the real-world application of the simulation framework. The results suggest that sharing information can reduce the deprivation costs in general. However, as the level of information sharing increases, its marginal effect on deprivation costs decreases. Setting up temporary shelters to limit the movement of affected people can help reduce uncertainty in demands, thereby facilitating the distribution of relief supplies. The distribution capacity is the primary factor affecting the performance of the three PD-HL structures. The results indicate that CANs are particularly effective in responding to catastrophic disasters due to their high distribution capacity. However, ACEs and PIEs can also perform well in situations where their distribution capacities are adequate to ensure that available supplies reach the affected population. In addition, the waiting time of disaster-affected people at the points of distribution (PODs) significantly affects the distribution of relief supplies, while walking speed has no significant impact on the distribution of relief supplies.

The impact of aquatic habitats on the malaria parasite transmission: A view from an agent-based model

In this paper, we develop an agent-based model (ABM) to study malaria disease transmission. This ABM is a host-vector model where hosts are humans and vectors are the female Anopheles mosquitos, moreover the ABM is spatially explicit integrating aquatic habitats in which mature vectors breed and the immature vectors develop. The dispersals of vectors, hosts and aquatic habitats and the interactions between them are explicit and depend on their geographical locations, the demographic processes for both hosts and vectors and hosts immunity are taken into account.We implement the ABM using parameters values corresponding to real conditions in nature. Starting from different initial conditions, ABM simulations provide quantitative results predicting disease prevalence in hosts and vectors.The obtained results in this study are asymptotic and highlight the role of aquatic habitats as a determining factor in the transmission of infection and the persistence of the disease.Finally, efficiency of the elimination of aquatic habitats is demonstrated to limit the disease transmission.

Integrated energy planning approach for accelerating energy transition of households

The success of energy transition planning depends on several influential and variable factors that need to be considered and tracked in modeling to support policymakers. Expert-based energy models are mainly developed following the assumptions of experts and, in most cases, a limited number of stakeholders. This top-down modeling approach will provide satisfactory results in centrally managed energy sectors (electricity generation and distribution, district heating, etc.), where further decisions could be projected relatively precisely. However, the same approach will not provide such certain outcomes in energy sectors where numerous heterogeneous individuals make decisions about the energy future. Households are a typical example of such a sector. The future structure of energy consumption in this sector, selection of energy source, or application of specific technology, especially in households with individual heating systems, introduce significant uncertainty in expert-based energy modeling and the creation of policy. Besides prices, the decisions of households are influenced by their attitudes, social environment, and incentive measures, etc. The influence of these factors on the adoption of sustainable heating by households can be examined using a bottom-up modeling approach, like the agent-based simulation model. The output of the agent-based simulation model provides useful data that can be further incorporated as one of the main assumptions in scenario development in an expert-based energy model. This paper offers an integration of these two approaches (bottom-up and top-down) for the early assessment of supporting measures and mechanisms for accelerating the energy transition in the household sector. The integrated approach is applied and tested for exploring pathways and effects of policy measures on the transition of individual heating in the household sector in Serbia.

The use of synthesised data for the development of Digital Twin: Chalmers student house case study

This research focuses on the development of a digital twin for a residential building using a synthesised data approach. The methodology involves five stages, with three of them dedicated to simulating different energy scenarios: actual energy consumption, passive house level consumption, and consumption after the implementation of smart building technology. The selected building is located at the Chalmers Technical University campus in Sweden. Synthesised data is used to simulate the energy demand of the building before and after renovation, as well as after the implementation of smart building technology. A custom agent-based simulation model is developed to simulate the impact of residents’ behaviours on the building’s energy consumption, and high-resolution data was analysed and synthesised to create a new dataset that was applied to the selected buildings. Finally, the results of the simulations were analysed and compared to assess the potential energy savings and improved energy performance achieved through the implementation of different scenarios. The study provides insights into the energy-efficiency of different measures for reducing energy consumption in residential buildings. The study provides insights into the energy-efficiency of different measures for reducing energy consumption in residential buildings. This research shows the potential of using synthesised data to assess and forecast changes in building stock transformation, even when real data are not available.

Emergency exit layout planning using optimization and agent-based simulation

Evacuation preparedness includes ensuring proper infrastructure, resources and planning for moving people from a dangerous area to safety. This is especially important and challenging during mass gatherings, such as large concerts. In this paper, we present the Emergency Exit Layout Problem (EELP) which is the problem of locating a given number of emergency exits and deciding their width such that the time it takes to evacuate the crowd from an arena is minimized. The EELP takes into account the geography of the arena and its surroundings, as well as the number of pedestrians in the crowd and the distribution of these within the arena. The EELP is formulated as a two-stage stochastic mixed integer linear program to handle the uncertainty related to the location of the possible incidents and the distribution of the pedestrians. Two cases are studied, a large concert planned at the Leangen trotting track in Trondheim and a smaller indoor arena. For each case, the EELP is solved for different scenarios, and the suggested layouts are evaluated using an agent-based simulation model. In particular, the potential of incorporating detailed assessment regarding the location and probability of specific incidents and the distribution of pedestrians are investigated. The computational study shows that making a more detailed risk assessment has little effect on the large concert, but a significant impact on the location of the emergency exits for the smaller indoor case. The results also indicate that it is more important to consider the location and probability of specific incidents rather than the pedestrian distribution.

Assessment of future parking systems with autonomous vehicles through agent-based simulation: A case study of Hangzhou, China

Autonomous vehicles (AVs) are expected to transform urban parking. AVs may not require dedicated parking spaces near their destination. Instead, AVs can drive to the optimal parking lot, cruise, or return to the origin after the passenger gets off. Previous research on AV parking strategies has largely overlooked actual trip patterns and road network conditions that can affect the effectiveness and feasibility of these strategies. In this paper, we construct an agent-based simulation model to assess the potential impacts of AVs on urban parking, focusing on new AV parking strategies. Using the real traffic network and trip data of Hangzhou, we assess the impacts on overall network efficiency, personal trip, and parking demand under different AV penetration rates. Results show that AVs could decrease the total parking cost from 23 to 6 RMB and increase parking convenience for both AVs and human-driven vehicles (HDVs). 21% of AVs will still choose to park, while 70% will return to the origin. On average, each trip with AV will generate an additional zero-occupant distance of 8.8 km, exacerbating the congestion. The findings indicate the benefits and challenges of new AV parking strategies, providing valuable insights for future urban parking planning and policy making.

Simulation Modelling for the Promotion of Green Residence Based on the Theory of Sustainability—Taking Jiangsu Province as an Example

Green residences have enormous potential for energy savings, emission reduction, and other comprehensive benefits, and their growth is crucial to achieving China’s carbon neutrality and carbon peaking targets. Nevertheless, at the moment, the national green residence is impacted by complicated factors at several levels, including government agencies, green residence builders, and green residence consumers, which results in the low-quality development of domestic green residences overall. As of 2020, 94% of all labeled green residences are design-label residences that can only be achieved during the design stage, while less than 10% are operational-label residences with stronger energy and emission-saving benefits. This causes the phenomenon of “green residences on the planning” to be serious. In order to accomplish the promotion of high-quality development of green residences and to promote green residences in China, this paper analyzes the influencing factors of green residence promotion from the multi-level perspective of macro-landscape signals, meso-collective agent green residences, and micro-individual agent consumers, based on the multi-level perspective (MLP) framework of sustainability theory. The paper subsequently builds a simulation model of green residence promotion using the agent-based system dynamics modeling method. Additionally, Jiangsu Province’s green residence promotion data are chosen for analogue simulation experiments, and the simulation results are also used to analyze the success conditions as well as the path to green residence promotion. This study demonstrates that (1) the agent-based simulation model of dynamics for the green residence promotion system has high reference value for the simulation of the promotion of green residences, and the model can clearly simulate the impact of micro-individual agent–consumer factors on the promotion of green residences; (2) in order to promote green residences, exterior landscape signals must be continuously improved; the stronger the landscape signals, the quicker the development of operationally labeled green residences; (3) priority is given to the development of two-star design-labeled green residences before 2035, and three-star operationally labeled residences will occupy the majority of the market after 2040. Meanwhile, the duration of landscape signals and the change in behavioral preferences of individual agents must be maintained for a long time.

Agent-based simulation model of panic buying behavior in urban public crisis events: A social network perspective

With the rapid growth of urban economies, accelerated urbanization has resulted in increasing urban public crisis events. The panic buying behavior of the public triggered by these events can seriously endanger social stability and the safety of the urban environment. Therefore, this study aims to explore the evolution of public panic buying behavior in complex urban systems with the intervention of multiple social actors. Based on information filtered from actual cases, this paper identifies 15 stakeholders associated with panic buying in complex urban systems. The complex urban network between the stakeholders is formalized using social network analysis (SNA). To more accurately reflect the evolutionary process of panic buying behavior, this paper proposes an agent-based model (ABM) that simulates five different behavioral states of public and the transition of states to explore the evolutionary process of panic buying behavior under other behavioral decisions of the government and the social media. The model explanation follows the ODD (Overview, Design concepts, Details) protocol. The findings suggest that, prioritizing limited government resources to enhance supply monitoring and responsive attitudes to the public can positively influence public panic buying behavior. At the same time, the social media's willingness to assume its social responsibility and improve information disclosure can further reduce the negative social impact of panic buying.

An agent-based simulation and logistics optimization model for managing uncertain demand in forest supply chains

This paper aims to model and minimize transportation costs in collecting tree logs from several regions and delivering them to the nearest collection point. This paper presents agent-based modeling (ABM) that comprehensively encompasses the key elements of the pickup and delivery supply chain model and presents the units as autonomous agents communicating. The modeling combines components such as geographic information systems (GIS) routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. ABM models the entire pickup and delivery operation, and modeling outcomes are presented by time series charts such as the number of trucks in use, facilities inventory, and travel distance. In addition, various simulation scenarios are used to investigate potential facility locations and truck numbers and determine the optimal facility location and fleet size.

Evaluating the Effectiveness of Potential Interventions for Guinea Worm Disease in Dogs in Chad Using Simulations.

Guinea worm (GW) disease (or dracunculiasis) is currently transmitted among dogs in Chad, which presents risks for the human population. We studied how interventions implemented at different levels might reduce the spread of GW disease (geographically and over time) and what levels of interventions might accelerate elimination. We built a multiple-water-source agent-based simulation model to analyze the disease transmission among dogs in Chad, as well as in geographic district clusters, and validated it using local infection data. We considered two interventions: 1) tethering, where infected dogs are kept on a leash during periods of infectivity, and 2) Abate®, under which the water source is treated to reduce infectivity. Our results showed that elimination (0 dog infections) is most likely achieved within 5 years with extremely high levels of tethering (95%) and Abate (90%), when intervention levels are uniform across district clusters. We used an optimization model to determine an improved strategy, with intervention levels which minimize the number of dogs newly infected in the 6th year, under limitations on intervention levels across clusters; the number of dogs infected after 5 years of intervention could be reduced by approximately 220 dogs with an optimized strategy. Finally, we presented strategies that consider fairness based on intervention resource levels and outcomes. Increased tethering and Abate resources above historical levels are needed to achieve the target of GW disease elimination; optimization methods can inform how best to target limited resources and reach elimination faster.

Computer Simulations of Ethics: The Applicability of Agent-Based Modeling for Ethical Theories

Abstract I consider the applicability of Agent-Based Modeling (ABM) and computer simulations for ethical theories. Though agent-based modeling is already well established in the social sciences, it has not yet found acceptance in the field of philosophical ethics. Currently, there are only a few works explicitly connecting ethics with agent-based modeling. In this paper, I show that it is possible to build computer simulations of ethical theories and that there are also potential benefits in doing so: (1) the opportunity for virtual ethical experiments that are impossible to do in real life, and (2) an increased understanding and appreciation of an ethical theory either through the programming implementation or through the visual simulation. In the first part of the paper, I mention some social science simulations with ethical import that could encourage ethicists to work with ABM. Second, I list the few pioneering works that attempt to combine computer simulation with philosophical ethics, the most prominent being Evolving Ethics: The New Science of Good and Evil (2010) by Mascaro et al. Third, I give pointers for the computer simulation of the most prominent ethical theories: deontological ethics, utilitarianism, feminist care ethics, and virtue ethics. In the final part, I consider the potential of using an existing reference model for the simulation of human behavior, the PECS model, as the foundation for a computer simulation of virtue ethics.

Cognitive digital twins for freight parking management in last mile delivery under smart cities paradigm

This paper examines the Freight Parking Management Problem (FPMP) of last-mile delivery within the context of Smart Cities where objects are managed by Digital Twins. Specifically, we investigate how Cognitive Digital Twins - Digital Twins with augmented semantic capabilities - can enhance instantly updated knowledge of parking connectivity to optimize logistics operations planning and urban resource allocation. We present a four-layer architectural framework to integrate individual logistics objects and systems into Smart Cities at a semantic level, with underlying enabling technologies and standards including Property Graph, Web Ontology Language (OWL), and Web of Things. Next, we conduct a case study of parcel delivery in Paris using a real-life Digital Twins platform called Thing in the future (Thing’in) by Orange France, coupled with an agent-based simulation model on AnyLogic, to demonstrate a real-world application of our approach. The results suggest that semantics-enabled Digital Twins connectivity can increase the comprehensive understanding of the delivery environment and enhance cooperation between heterogeneous systems, ultimately resulting in improved logistics efficiency, reduced negative externalities, and better utilization of resources. Furthermore, this work showcases potential new business services for logistics service providers and provides managerial insights for city planners and municipal policymakers. An actual mobile application prototype is presented to showcase the applicability of the work.

Scaling up Renewable Energy Financing through Islamic Blended Finance: Case Study in Indonesia

This paper outlines to explore the influencing factors of unachieved target of the energy mix from renewable energy (RE) sources in Indonesia, to develop an integrated Islamic blended finance model for financing RE projects in Indonesia, and to analyse the costs and benefits of the proposed model. This research employs an exploratory sequential mixed method with the qualitative phase through in-depth interviews and Focus Group Discussions (FGD) and quantitative phases using primary and secondary data to simulate the Agent-Based Model (ABM) of the proposed model. The paper found three main financing issues on RE sector in Indonesia, including high investment costs and long payback period of RE projects, high risk perception to finance RE projects, and the uncertainty of investment return for RE developers. Besides, based on ABM simulation, Islamic blended finance can contribute to reduce the required period to achieve the energy mix target. Despite the less familiarity of Islamic finance instrument for the RE sector, an initial of a pilot project in place of the proposed business model might increase common awareness regarding this potential. Hence, policy implication delivered in this paper should be a key component to scale up RE sector via Islamic blended finance instrument.

Towards economic coordination mechanisms design

The study presents an approach that allows one to analyze various methods and types of socio-economic coordination from a unified methodological standpoint and to develop tools for designing coordination mechanisms. The basic foundations and prerequisites necessary for a unified description of various types of socio-economic coordination are analyzed. A conceptual model and a three-step algorithm for designing coordination mechanisms are proposed. Known methods of economic coordination are considered as a result of designing in accordance with the proposed algorithm. The results obtained have various theoretical and applied applications. On this basis agent-based simulation models can be created both for individual economic coordination mechanisms and for an economic system with built-in coordination mechanisms.

AGENT-BASED SIMULATION OF HURRICANE RISK INFORMATION DISSEMINATION

The effectiveness of official warning dissemination and interpretation determines the adoption of protective actions in response to hurricane threat. This paper describes the development of a conceptual warning diffusion model that articulates the whole process of the hurricane warning diffusion and individual decision-making, drawing on emergency management and social science bodies of literature. This conceptual model is then partially encoded into a NetLogo agent-based simulation model to demonstrate the effects of various warning diffusion components on evacuation decisions for areas of interest. The translation of the conceptual model into agent-based simulations can inform the coastal engineering and science communities of the importance of communicating risk information to the public, and also helps to identify future research needed to illuminate hurricane risk communication pathways.

Identification of influence of digital twin technologies on production systems: a return on investment-based approach

The object of this study is the impact of different digital twin solutions on the performance of job-shop manufacturing systems, while economic aspects are also taken into consideration. This paper proposes an approach to analyze the impact of different identification systems on the efficiency and ROI of digital twin deployment in production systems. In order to achieve this aim, let’s analyze the investment and operation cost of different Internet of Things technologies. The next phase of the research work was the definition of performance parameters, which makes it possible to analyze the impact of different digital twin solutions on the productivity of the job-shop manufacturing system. It is possible to choose four financial indicators to analyze the economic impact of digital twin solution on job-shop manufacturing: return on investment, compound annual growth rate, internal rate of return and net present value. Our approach is based on a novel agent-based simulation model using AnyLogic simulation tool. From the results of this productivity analyses, the model computes the financial indicators, which describe the expected financial impact of the investment and operation cost. It is compared the impact of barcodes and radiofrequency identification technologies on the financial and technological impact of the job-shop manufacturing environment. The numerical analysis of a job-shop manufacturing system shows, that the radiofrequency identification-based digital twin solution has 9.2 % higher return on investment, 53 % higher net present value and 1.6 % higher compound annual growth rate. The model can be easily converted to analyze other types of manufacturing systems, which can lead to increased efficiency of digital twin solutions

The Impact of the Good Behavior Game on Risk for Drug Use Disorder in an Agent-Based Model of Southern Sweden.

Drug use disorder (DUD) is a worldwide problem, and strategies to reduce its incidence are central to decreasing its burden. This investigation seeks to provide a proof of concept for the ability of agent-based modeling to predict the impact of the introduction of an effective school-based intervention, the Good Behavior Game (GBG), on reducing DUD in Scania, Sweden, primarily through increasing school achievement. We modified an existing agent-based simulation model of opioid use disorder to represent DUD in Scania County, southern Sweden. The model represents every individual in the population and is calibrated with the linked individual data from multiple sources including demographics, education, medical care, and criminal history. Risks for developing DUD were estimated from the population in Scania. Scenarios estimated the impact of introducing the GBG in schools located in disadvantaged areas. The model accurately reflected the growth of DUD in Scania over a multiyear period and reproduced the levels of affected individuals in various socioeconomic strata over time. The GBG was estimated to improve school achievement and lower DUD registrations over time in males residing in disadvantaged areas by 10%, reflecting a decrease of 540 cases of DUD. Effects were considerably smaller in females. This work provides support for the impact of improving school achievement on long-term risks of developing DUD. It also demonstrated the value of using simulation modeling calibrated with data from a real population to estimate the impact of an intervention applied at a population level.