Agent-based Simulation Approach Research Articles

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

An Agent-Based Simulation Approach for Urban Road Pricing Considering the Integration of Autonomous Vehicles With Public Transport

An Agent-Based Simulation Approach for Urban Road Pricing Considering the Integration of Autonomous Vehicles With Public Transport

Synchromodal transport re-planning: an agent-based simulation approach

In the rapidly evolving global marketplace, the logistics sector faces a multitude of challenges that demand implementation of more resilient solutions to respond to any future disturbance. Synchromodal transport, which is viewed as an extension of multimodal transport, is known as a key answer to this issue, as it provides more flexible and sustainable freight transport and also focuses on collaboration between different logistics players. We consider synchromodal transport as a collection of agents that not only have their own characteristics and behaviors, but also interact with each other, which impacts the entire system. In this paper, we study the system using an Agent-Based Modeling approach. The network represents the combination of long-haul and drayage transport, where pre-haulage and end-haulage are done only by truck, and the rest can be done by trucks, trains, or barges. A numerical experiment is conducted to evaluate cost savings and emissions reduction under different logistics service providers’ relation and re-routing scenarios. Our findings show that synchromodal scenarios are more economically and environmentally efficient, and that they lead to higher flexibility and reliability compared to business-as-usual scenarios. Additionally, our model verifies that the cost saving is considerable when logistics service providers collaborate with each other. The results of sensitivity analyses show consistent overall trends when comparing the different scenarios. Therefore, the conclusions drawn from the original experiment appear to be applicable, not only for that specific instance, but have broader relevance and applicability.

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.

Service and inventory model for maintenance workshop in the short cycle operation region: Agent-based simulation approach

Tools used in the North Sea region has a high cycle and come very often to maintenance workshop to be checked, repaired if needed and prepared for the next operations. The availability of spare parts at the workshop plays a significant role to keep the flow time as short as possible and meet such high-cycle operations. Supplying spare parts from the best-cost countries to the North Sea region with about one-year lead time makes the situation more critical and the economic order quantity and reorder point need to be found. However, having inventory at the workshop and ordering a batch of spare parts increases operational expenditures. Moreover, frequent supplies increase the environmental impacts of shipping CO2 and spare scrap rates, whereas repairing the used spare parts and reusing them again can offer a more sustainable solution. Therefore, the purpose of this paper is to develop a simulation model that can quantify the cost and benefits of reusing repaired spares compared to supplying newly built spares from the best-cost countries. To achieve this purpose, a case study has been implemented on a specific maintenance workshop within the North Sea region and the entire tool repair and spare part supply operations are conceptualized and modelled with the help of the simulation modelling approach. Two scenarios have been simulated: (1) the maintenance workshop fully depends on supplying newly built spares from the best-cost countries with no inventory stock or spare reuse, and (2) the Maintenance workshop is primarily dependent on repaired spares with an optimal level of spares stock. The simulation results, from the studied case, support the second scenario where a repair path cycle is introduced to the maintenance workshop, as a 78% reduction in lead times, a 116% improvement in worker utilization, a 73% reduction in crowding levels, a 52% reduction in scrap rate, and a potential profit increase of roughly three million NOK (20%). Therefore, it can be concluded that a local repair service is required to keep maintenance workshops in high-cycle regions at high-performance levels.

Identifying Impacts of School-Escorted Trips on Traffic Congestion and the Countermeasures in Bangkok: An Agent-Based Simulation Approach

Identifying Impacts of School-Escorted Trips on Traffic Congestion and the Countermeasures in Bangkok: An Agent-Based Simulation Approach

Understanding bike-sharing as a commute mode in Singapore: An agent-based simulation approach

Understanding bike-sharing as a commute mode in Singapore: An agent-based simulation approach

An Agent-Based Decision Support Framework for a Prospective Analysis of Transport and Heat Electrification in Urban Areas

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users’ behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents’ behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities.

Establishing industrial community energy systems: Simulating the role of institutional designs and societal attributes

The importance of decreasing industrial CO2 footprints has become evident, as also highlighted in COP26. As such, the transition to renewable energy in the industrial sector is essential to meet the targets. To this aim, establishing industrial community energy systems (InCES) where industries collectively invest in a shared energy system is an economically and environmentally attractive option. Yet, the emergence and continuity of such collective initiatives among industrial companies has neither received considerable attention in the scientific literature nor in practice. This research, as the first of its kind, aims to investigate institutional design options that allow for such collaboration to take place for the establishment and continuity of an InCES. Given the bottom-up and collaborative nature of such initiatives, we take an agent-based modeling and simulation approach, for the first time in this area, that incorporates the institutional and societal attributes that influence the formation and continuation of an InCES. We take data from an industrial cluster in Arak, one of the most prominent industrial cities in Iran. The results of this study confirm the economic feasibility of an InCES as compared to individual renewable energy investment in the cluster. The results also highlight the importance of flexible membership in increasing the number of investors (i.e., industrial companies) in such initiatives. Other important recommendations are: considering the installation of at least 15% extra capacity for the powerplant, restricting electricity consumption and enforcing on-time payment of monthly premium fees.

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.

Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach

Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road users, particularly in densely populated urban cities. Since road capacity in urban cities changes throughout the day, an optimal traffic light signal duration might be different. Hence, in this paper, the effect of vehicle composition and traffic light phases on traffic flow during peak and off-peak hours in Georgetown, Penang, one of the highly populated cities in Malaysia, is investigated. Through Agent-Based Modelling (ABM), this complex system is simulated by integrating the driver’s behaviour into the model using the GIS and Agent-Based Modelling Architecture (GAMA) simulation platform. The result of predicted traffic flow varies significantly depending on the vehicle composition while the duration of the traffic signal timing has little impact on traffic flow during peak hours. However, during off-peak hour, it is suggested that 20 s duration of green light provides the highest flow compared to 30 s and 40 s duration of green light. This concludes that the planning for traffic light phasing should consider multiple factors since the vehicle composition and traffic light timing for an effective traffic flow varies according to the volume of road users.

An agent-based simulation approach to investigate the shift of Switzerland’s inland freight transport from road to rail

An agent-based simulation approach to investigate the shift of Switzerland’s inland freight transport from road to rail

Examining the effects of Automated Mobility-on-Demand services on public transport systems using an agent-based simulation approach

Examining the effects of Automated Mobility-on-Demand services on public transport systems using an agent-based simulation approach

Self-reconfiguration simulations of turbines to reduce uneven farm degradation

This study explored the concept of a floating offshore wind farm (FOWF) that self-reconfigures wind turbine (WT) positions. The self-reconfiguration (SR) mechanism moves degraded turbines to different farm positions to delay failures occurring and reduce power loss. A 40-turbine agent-based simulation was created utilising wind and turbine performance data. For the first time, the SR mechanism was designed and optimised with principles of self-engineering complexity. The paper demonstrates the effectiveness of the SR mechanism through an agent-based simulation approach. The optimised SR was used in FOWF simulations of 50 years of operation; SR balanced fatigue across the FOWF and led to an increased income of £5–40M at years 27–33 of operation; however, outside of these years, there is no net positive income, and by year 50 SR has cost the FOWF £4–7M more in movement costs. Lastly, simulations with repair and maintenance restricted to 10 months or less were conducted. SR delayed turbine failures, so they occurred in months when repair could be conducted. The SR reduced power loss and increased net income by up to £20M, indicating that SR could be useful when repair and maintenance times are limited. In the absence of significant operational data, a qualitative validation with experts confirmed the approach and the validity of the simulation model for a range of FOWF scenarios.

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.

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

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

Budget Processes Modeling: Agent-Based Approach

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

Computational Model of Recommender System Intervention

A recommender system is an information selection system that offers preferences to users and enhances their decision-making. This system is commonly implemented in human-computer-interaction (HCI) intervention because of its information filtering and personalization. However, its success rate in decision-making intervention is considered low and the rationale for this is associated with users’ psychological reactance which is causing unsuccessful recommender system interventions. This paper employs a computational model to depict factors that lead to recommender system rejection by users and how these factors can be enhanced to achieve successful recommender system interventions. The study made use of design science research methodology by executing a computational analysis based on an agent-based simulation approach for the model development and implementation. A total of sixteen model concepts were identified and formalized which were implemented in a Matlab environment using three major case conditions as suggested in previous studies. The result of the study provides an explicit comprehension on interplaying of recommender system that generate psychological reactance which is of great importance to recommender system developers and designers to depict how successful recommender system interventions can be achieved without users experiencing reactance and rejection on the system.

How institutional pressures improve environmental management performance in construction projects: an agent-based simulation approach

How institutional pressures improve environmental management performance in construction projects: an agent-based simulation approach

Understanding decentralization of decision-making power in proof-of-stake blockchains: an agent-based simulation approach

ABSTRACT Blockchain systems allow for securely keeping shared records of transactions in a decentralised way. This is enabled by algorithms called consensus mechanisms. Proof-of-work is the most prominent consensus mechanism, but environmentally unsustainable. Here, we focus on proof-of-stake, its best-known alternative. Importantly, decentralised decision-making power is not an inherent feature of blockchain systems, but a technological possibility. Numerous security incidents illustrate that decentralised control cannot be taken for granted. We therefore study how key parameters affect the degree of decentralisation in proof-of-stake blockchain systems. Based on a real-world implementation of a proof-of-stake blockchain system, we conduct agent-based simulations to study how a range of parameters impact decentralisation. The results suggest that high numbers of initial potential validator nodes, large transactions, a high number of transactions, and a very high or very low positive validator network growth rate increase decentralisation. We find weak support for an impact of changes in transaction fees and initial stake distributions. Our study highlights how blockchain challenges our understanding of decentralisation in information systems research, and contributes to understanding the governance mechanisms that lead to decentralisation in proof-of-stake blockchain systems as well as to designing proof-of-stake blockchain systems that are prone to decentralisation and therefore more secure.