Multi-agent Simulation Research Articles

Exploring the Effects of Blockchain Scalability Limitations on Performance and User Behavior in Blockchain-Based Shared Manufacturing Systems: An Experimental Approach

This study investigates the effects of blockchain technology scalability limitations on the performance of Blockchain-Based Shared Manufacturing (BBSM), an innovative smart-manufacturing paradigm aimed at enhancing the utilization of global manufacturing resources via peer-to-peer (P2P) collaboration of self-organized manufacturing assets. Despite the prevalence of research highlighting blockchain technology’s scalability limitations as the main barrier for adoption, few studies have explored their effects on the operation of blockchain-based systems. The primary goal of the presented research work is to explore the implications of blockchain technology scalability limitations on the BBSM system’s performance and user behavior. To obtain realistic behavior, an experiment is conducted using an online game played by human participants. Analysis of the players’ strategy is used for implementation of a multi-agent simulation model, which is then employed to assess the influence of varying blockchain network configurations on the BBSM concept’s performance. Preliminary experimental findings reveal that a congested blockchain network leads to increased transaction costs and reduced service prices, consequently devaluing the manufacturing role in the BBSM system and causing underutilization of existing maximum production capacities. Moreover, allocating funds to financial activities rather than manufacturing activities yields superior outcomes for system users. Simulation results indicate that the BBSM system’s response to alterations in blockchain network throughput is contingent upon the production function. The findings of this study reveal that the scalability limitations of blockchain technology impair the performance of the BBSM system and affect user behavior in the system, underscoring the necessity for future research to concentrate on incorporating scalable solutions within blockchain-based manufacturing systems.

Electric bus charging facility planning with uncertainties: Model formulation and algorithm design

This paper investigates the electric bus charging facility planning (EB-CFP) problem for a bus transit company operating a heterogeneous electric bus (EB) fleet to provide public transportation services, taking into account uncertainties in both EB travel time and battery degradation. The goal of the EB-CFP problem is to determine the number and type of EB chargers that should be deployed at bus terminals and depots to meet daily EB charging demand while minimizing total cost. The problem is formulated as a two-stage stochastic programming model, with the first stage determining the EB charger deployment scheme and the second stage estimating the EB daily operational cost with respect to a predetermined EB trip timetable for a given EB charger deployment scheme. To effectively address the second stage problem, a multi-agent EB transit simulation system that mimics the daily EB operation process is developed. We then design two heuristic methods, the reinforcement learning (RL)-based method and the surrogate-based optimization (SBO), that use the developed multi-agent EB transit simulation system to solve the two-stage stochastic programming model for large-scale instances. Lastly, we run a series of experiments on a fictitious EB transit network and a real-world EB transit network in Singapore to evaluate the performance of the models and algorithms. In order to improve the performance of the EB transit system, some managerial insights are also provided to urban bus transit companies.

A Comparative Analysis of Two Pricing Mechanisms, MCP and PAB, in the Chinese Frequency Regulation Market

In the face of the new reform of China’s “unified electricity market”, the frequency regulation ancillary service market in each region of China is still divided between using the MCP and PAB pricing mechanisms. The purpose of this paper is to explore the different impact the laws of the above two pricing mechanisms have on the frequency regulation market and to provide relevant suggestions for this electricity market reform. This paper simulates the competitive activities of the frequency regulation market based on the multi-agent simulation model, conducts a comparative experiment by changing the pricing mechanism to a single variable based on the rules of the Sichuan frequency regulation market in China, and concludes that MCP can make the market fast and stable, and that its market settlement price is low, which is suitable for the “unified electricity market”. Although PAB makes the market settlement price high, it can ensure the retention of high-performance units in the market, and the stable settlement price makes this model able to accurately reflect the “price signal”, making it suitable for late adoption in the “unified electricity market”.

Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique

Virtual coupling (VC) is a train-centric next generation signalling system, which can enable multiple trains to operate in a formation just like one train or decouple separately on-the-run or at station flexibly or as planned. With the aim of optimizing the interdeparture train headway time, providing the variable capacity for diverse passenger demand, maximizing the passenger riding comfort degree, and minimizing passenger travel cost and train operation cost, the dynamic schedule for VC-enabled rail transit services is investigated with the multiagent simulation technique on NetLogo platform. Our contribution is mainly fourfold. First, VC-enabled rail transit entity for simulation is represented with the multiagent technique, including representation of train unit, train convoy, passenger attributes and behavior, and mathematical formula for calculation of the train operation cost and passenger travel cost, as well as passengers riding comfort degree are proposed. Second, the operational principles for flexible and self-organisingVC-enabled trains are defined. Third, the VC-enabled train-centric, passenger demand-driven, and agent-based simulation flow and algorithms are developed innovatively, which adopt the ergodic strategy for simulation by traversing each O-D pair demand along each route section over the rail transit network. Finally, we test and discuss the proposed methodology on the designed computational experiment on the NetLogo platform, and the simulation results series validated the effectiveness of the proposed methodology. The provided research can effectively support the VC-enabled platoon operation-oriented train service schedule for future study.

Anomaly Detection using Generative Adversarial Network VAE

Fraudulent credit card transactions continue to be one of the problems facing businesses and banks. It causes us to lose billions of dollars each year. Designing efficient algorithms is one of the most important challenges in this field. This paper aims to propose an efficient approach that automatically detects fraud in credit card transactions using Generative Adversarial Network Variational Auto encoders. The effectiveness of the proposed method (Generative Adversarial Network VAE) has been proved in identifying fraud in actual data from transactions made by credit cards. However, the typical credit card data set presents an imbalanced classification landscape due to highly skewed class distributions. Researchers have proposed several strategies to address these imbalances, but draw backs still remain. The proposed method is tested on an open credit card fraud dataset, which contains 20 million transactions generated from a multi-agent virtual world simulation performed by IBM. Experimental results show that the VAE method performs better than traditional deep neural network methods. Through experiments, compared with the VAE and traditional fully connected neural networks, the results showed the proposed algorithm improves the classification accuracy of a minority class of imbalanced datasets.

Differential Assessment of Strategies to Increase Milk Yield in Small-Scale Dairy Farming Systems Using Multi-Agent Modelling and Simulation

Multi-agent-based modelling and simulation provides an adequate environment to study the real world. This paper presents the use of a multi-agent research and simulation (MARS) framework and model design based on the overview, design concepts, design (ODD) protocol to model and simulate small-scale management strategies that are important for increased milk yield per cow. In reality, strategies for farm management at a small-scale level are purely based on heuristics that cost farmers and lead to inadequate milk yields. A differential assessment of the farming strategies was conducted to yield a data-driven approach for selection of the best strategies, which in turn will optimize investments and increase milk yield. The agent-based modelling and simulation revealed that, the studied strategies based on income, farm, and farmer-based characteristics influenced an increase of up to 7.72 L of milk above the average (12.7 ± 4.89). Generally, there was an increase in milk yield based on the identified evolvement strategies; from a baseline data average milk yield of 12.7 ± 4.89 to simulated milk yield average of 17.57 ± 0.72. Evaluating the agent-based models in real-world scenarios will strengthen the assurance that the identified strategies can move small-scale dairy farmers from low to higher milk producers.

A Personality-based Model of Emotional Contagion and Control in Crowd Queuing Simulations

Queuing is a frequent daily activity. However, long waiting lines equate to frustration and potential safety hazards. We present a novel, personality-based model of emotional contagion and control for simulating crowd queuing. Our model integrates the influence of individual personalities and interpersonal relationships. Through the interaction between the agents and the external environment parameters, the emotional contagion model based on well-known theories in psychology is used to complete the agents’ behavior planning and path planning function. We combine the epidemiological SIR model with the cellular automaton model to capture various emotional modelling for multi-agent simulations. The overall formulation involves different emotional parameters, such as patience, urgency, and friendliness, closely related to crowd queuing. In addition, to manage the order of the queue, governing agents are added to prevent the emotional outbreak. We perform qualitative and quantitative comparisons between our simulation results and real-world observations on various scenarios. Numerous experiments show that reasonably increasing the queue channel and adding governing agents can effectively improve the quality of queues.

Assessing the effectiveness of intervention policies for reclaimed water reuse in China considering multi-scenario simulations

The reclaimed water is an effective tool for water environmental management. And in China, government intervention is regarded as a powerful instrument to realize its large-scale utilization due to the public welfare and social characteristics. Therefore, the tripartite evolutionary game and multi-agent simulation system were combined to explore how intervention policies, such as price subsidies and regulation of water information disclosure, can influence the behaviors of stakeholders involved in the reclaimed water market, so as to alleviate water environment crisis. The results showed that: (1) The incentive of price subsidy shows noticeable regional differences due to different initial reclaimed water utilization rates, and the policy effect on regions with medium level utilization (25%–40%) > regions with high utilization (higher than 40%) > regions with low utilization (lower than 25%); (2) enhancing water information supervision can positively regulate the stimulation of subsidy policy in regions with low wastewater reuse; (3) the intensity of price subsidies has an inverted U-shaped relationship with the market stability in regions with high wastewater reuse; (4) diverse regulatory models on water information disclosure show various chain effects on improving wastewater reuse.

The impact of dispatching logic on the efficiency of Urban Air Mobility operations

The application of electrified propulsion systems is expected to facilitate the future operation of small aircraft in urban and suburban areas. However, in addition to the safe design of vehicles, efficient operation must also be achieved by matching passenger requests to unoccupied and suitable vehicles. In the agent-based transportation simulation MATSim (Multi-Agent Transport Simulation), a rule-based approach has been followed by assigning the nearest available vehicle to each passenger request. Sophisticated considerations such as pooling multiple passengers with identical trip requests in terms of time and location or the urgency of a request were only partially considered or not considered at all. To decrease operating costs, especially by reducing the number of passenger flights and avoiding relocation flights, appropriate dispatching is essential for efficient assignment of available vehicles to trip requests. For this purpose, the suitability of the Bipartite Matching Dispatcher (BMD), already proven for ground-based on-demand mobility concepts, was investigated for application to UAM systems by demonstrating its performance on an existing data set using a mock-up simulation and comparing it to the initial dispatcher in MATSim. Studies have not only shown the similarity between UAM and ground-based on-demand operations, but also the compatibility of BMD for high load traffic scenarios with undersized fleets. Insights on the impact of available information, for example, the prioritization of early-submitted requests over urgent requests, on the operator’s point of view can be concluded. For an efficient vehicle assignment, the consideration of passengers’ attitude towards possible comfort restrictions due to longer waiting times and incentives through price adjustments is of crucial importance.

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.

Consequence Assessment Model for Gasoline Transport: Belgrade Case Study Based on Multi-Agent Simulation

The transport of dangerous goods by road represents a specific problem, considering that these vehicles can have great negative effects on people and the environment in the event of an accident. In the last few decades, various methods have been defined for risk management in this area. Most of these methods are based on reducing the probability and the consequences of an accident. In a large number of cases, the probability of the occurrence is in much greater focus than the consequences. To fill this gap, we created a new model for assessing the consequences on the population in the event of a fire during gasoline transportation. The model is based on the hypothesis that hazard intensity is directly proportional to the intensity of the heat generated by the fire and inversely proportional to the distance from the accident. The model was implemented through a Belgrade case study with multi-agent simulation. The results of the paper show that there is a 43% probability that there would be between 11 and 28 casualties in the observed location in the event of an accident. This confirms the initial hypothesis and shows that the model can be successfully used to assess the consequences.

Data Analytics and Visualization of Adaptive Collaboration Simulations

Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.

Mitigating Catastrophic Forgetting with Complementary Layered Learning

Catastrophic forgetting is a stability–plasticity imbalance that causes a machine learner to lose previously gained knowledge that is critical for performing a task. The imbalance occurs in transfer learning, negatively affecting the learner’s performance, particularly in neural networks and layered learning. This work proposes a complementary learning technique that introduces long- and short-term memory to layered learning to reduce the negative effects of catastrophic forgetting. In particular, this work proposes the dual memory system in the non-neural network approaches of evolutionary computation and Q-learning instances of layered learning because these techniques are used to develop decision-making capabilities for physical robots. Experiments evaluate the new learning augmentation in a multi-agent system simulation, where autonomous unmanned aerial vehicles learn to collaborate and maneuver to survey an area effectively. Through these direct-policy and value-based learning experiments, the proposed complementary layered learning is demonstrated to significantly improve task performance over standard layered learning, successfully balancing stability and plasticity.

Understanding dynamics of polarization via multiagent social simulation

It is widely recognized that the Web contributes to user polarization, and such polarization affects not just politics but also peoples’ stances about public health, such as vaccination. Understanding polarization in social networks is challenging because it depends not only on user attitudes but also their interactions and exposure to information. We adopt Social Judgment Theory to operationalize attitude shift and model user behavior based on empirical evidence from past studies. We design a social simulation to analyze how content sharing affects user satisfaction and polarization in a social network. We investigate the influence of varying tolerance in users and selectively exposing users to congenial views. We find that (1) higher user tolerance slows down polarization and leads to lower user satisfaction; (2) higher selective exposure leads to higher polarization and lower user reach; and (3) both higher tolerance and higher selective exposure lead to a more homophilic social network.

Tenerife’s Infrastructure Plan for Electromobility: A MATSim Evaluation

According to the Canarian government’s plans, a complete decarbonization of the Canary Islands economy is foreseen from 2040 onwards, which includes the electrification of land transport in the archipelago. However, due to the current low penetration rate of electric vehicles (EVs) on the islands, the number of EVs in circulation is expected to grow significantly in the coming years. Despite this, the network of charging points in Tenerife is currently totally insufficient, which is why it is essential to carry out a study to design the network of charging points in such a way that it can absorb the entire fleet of EVs that is expected to be in place by 2040. To this end, there are studies on the capacity, in terms of parking space, available for the installation of these charging points, but to date there are no studies on this subject supported by mobility data. For this reason, a simulation of traffic in Tenerife in 2040 has been carried out using MATSim (Multi-Agent Transport Simulation) to determine the ideal places to install these charging points and to find the number of charging points needed for the network.

Emergency evacuation time model of nuclear power plant pedestrians

The construction of nuclear power plant is usually multi-phase. In the coexisting period of operation and construction, the types of pedestrian in nuclear power plant are complicated. Like Fukushima accident, nuclear power plant accident is dangerous and easy to cause mass panic. In order to improve the efficiency of emergency evacuation in nuclear power plant and accurately obtain the evacuation time of nuclear power plant pedestrians, a pedestrian evacuation time model is established. Firstly, by studying the general layout characteristics of nuclear power plant, nuclear power plant is divided into three areas, protected area, management area and construction area. Secondly, the pedestrian flow movement monitoring data of nuclear power plant are analyzed to obtain the pedestrian movement characteristics and speed-density curves of different locations(facilities) in different areas of the plant, which will provide data support for establishing the pedestrian evacuation time model of nuclear power plant. Thirdly, the evacuation time of nuclear power plant is divided into three parts: pedestrian reaction time, pedestrian walking time and pedestrian waiting time. The evacuation time model of nuclear power plant pedestrian is obtained by analyzing and modeling these three parts respectively. Finally, taking a nuclear power plant as an example, three types of evacuation areas are set up according to the layout characteristics and construction status of nuclear power plant. The model was used to calculate the evacuation time of different types of areas. Based on multi-agent pedestrian flow simulation method, emergency pedestrian flow simulation of three types of evacuation areas is obtained. The results of the model are compared with the simulation results, and it is found that the model has good matching in different nuclear power plant areas, which verifies the rationality and accuracy of the model.

Analysis of Charging Infrastructure for Private, Battery Electric Passenger Cars: Optimizing Spatial Distribution Using a Genetic Algorithm

To enable the deployment of battery electric vehicles (BEVs) as passenger cars in the private transport sector, suitable charging infrastructure is crucial. In this paper, a methodology for the efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a 100% market penetration of BEVs of (around 1.3 million vehicles) in Berlin, Germany. The goal of the evaluated methodology is the development of various charging infrastructure scenarios—including public and private charging—which are suitable to cover the entire charging demand. Therefore, these scenarios are investigated in detail with a focus on the number of public charging points, their spatial distributions, the available charging power, and the necessary capital costs. For the creation of these charging infrastructure scenarios, a placement model is developed. As input, it uses the data of a multi-agent transport simulation (MATSim) scenario of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital costs of the charging points and the mean detour car drivers must undertake are used as the optimization criteria. Using these criteria, we expect to generate cost-efficient infrastructure solutions that provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found, and suitable solutions can be selected by subsequently using other criteria. Besides the generated charging scenarios for Berlin, the main goal of this paper is to provide a valid methodology, which is able to use the output data of an agent-based, microscopic transport simulation of an arbitrary city or area (or even real driving data) and calculate different suitable charging infrastructure scenarios regarding the different optimization criteria. This paper shows a possible application of this method and provides suggestions to improve the significance of the results in future works. The optimized charging infrastructure solutions for the Berlin scenario show capital costs of between EUR 624 and 2950 million. Users must cover an additional mean detour of 254 m to 590 m per charging process to reach an available charging point. According to the results, a suitable ratio between the charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50 kW) of less than ten percent seems to be sufficient if it is situated at the main traffic routes and highly frequented places.

Micro-Household Human Capital Investment Decisions and a Simulation Study from the Intergenerational Conflict Perspective.

Education is highly valued in Asian families. However, as family members age, competition for intra-family resources affects children's actualization in the family, which impacts the family's future capital. However, most existing studies have interpreted the family's intergenerational conflicts in terms of care services for older adults, and few have analyzed and simulated intra-family competition based on the intergenerational conflict. This study introduces a multi-agent simulation approach to observe micro-households' educational investment choices under the dual pressures of retirement and childcare. This measure captures households' investment choices and provides a decision basis for given households. Using data from the China Family Panel Study for 2014, 2016, and 2018, we explore the impact of these dual pressures on household educational expenditures and their differences across urban and rural areas, household aging, and income samples. We also simulated the micro-households' investment choices under these dual pressures to observe that these pressures reduce investments in educational human capital in these "sandwich-like" households. The simulation results suggest that households with high childcare stress invest more in education than those with a high retirement burden. Moreover, income growth can mitigate the dual stress "crowding-out" effect on education, which is most pronounced in low-income, high childcare-stress households.

A protocol for analysing the role of shared situational awareness and decision-making in cooperative disaster simulations

Acute environmental emergencies and disasters call for multi-organisational collaboration where each individual response agency has their own responsibilities and specific roles in an emergency operation. However, different operational systems, cultures and norms between the response agencies can hamper such collaboration and the formation of shared situational awareness (SA). SA has an impact on how an individual response agency understands the situation and therefore forms the basis for decision-making in emergency and disaster management. The premise of this paper is that formation of shared SA is a key to improving preparedness to deal with disasters. The effective formation can be facilitated by creating shared understanding of how disaster management should be implemented. We developed a protocol for analysing the creation of shared SA and its significance for successful operational decision-making during multi-agent disaster simulations. As a starting point, based on a literature review, we built a systemic framework to represent the process of acute operational decision-making, describing particularly the role of the shared SA creation as part of the process. Based on the framework, we then developed a protocol for monitoring the formation and analysing the magnitude of shared SA, how it is linked to the decision making process and the execution of the actions during the exercises. We suggest the protocol can be applied in various emergency and disaster preparedness contexts to support the development and harmonisation of multi-agent and multi-organisational training and future crisis response practises.

Simulation Study on the Coupling Relationship between Traffic Network Model and Traffic Mobility under the Background of Autonomous Driving

Autonomous driving technology will bring revolutionary changes to the development of future cities and transportation. In order to study the impact of autonomous driving on urban transportation networks, this paper first summarizes the development status of autonomous driving technology, and then three space–traffic network coupling models are proposed based on the differences of speed and space, which are the traditional difference type, scale variation type, and slow-guided type. On this basis, a new 4 * 4 km grid city model is constructed. Based on the MATSim multi-agent simulation method, the traffic parameters of the three models are studied. The results show that under the same traffic demand, the service scale and level of the three traffic networks are significantly different. The optimal service level of the traditional differential type is 2.15 times the efficiency of the slow-guided type. Under the same demand and road network mode, the travel speed of the autonomous driving mode is 1.7–2.8 times that of the traditional mode. Under the same lane area ratio, the travel speed of traditional driving is much smaller than that of autonomous driving, which is about 2.6–3.6 times greater than the former. The research conclusion has certain reference significance for formulating urban spatial development strategies and policies under autonomous driving environments and for promoting the sustainable development of urban transportation.