Agent-based Simulation Approach Research Articles

BISTRO

The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state of the urban mobility landscape do not yet exist. Furthermore, few approaches have enabled the massive ingestion of urban data in planning tools capable of offering the flexibility of scenario-based design. This article introduces Berkeley Integrated System for Transportation Optimization (BISTRO), a new open source transportation planning decision support system that uses an agent-based simulation and optimization approach to anticipate and develop adaptive plans for possible technological disruptions and growth scenarios. The new framework was evaluated in the context of a machine learning competition hosted within Uber Technologies, Inc., in which over 400 engineers and data scientists participated. For the purposes of this competition, a benchmark model, based on the city of Sioux Falls, South Dakota, was adapted to the BISTRO framework. An important finding of this study was that in spite of rigorous analysis and testing done prior to the competition, the two top-scoring teams discovered an unbounded region of the search space, rendering the solutions largely uninterpretable for the purposes of decision-support. On the other hand, a follow-on study aimed to fix the objective function. It served to demonstrate BISTRO’s utility as a human-in-the-loop cyberphysical system: one that uses scenario-based optimization algorithms as a feedback mechanism to assist urban planners with iteratively refining objective function and constraints specification on intervention strategies. The portfolio of transportation intervention strategy alternatives eventually chosen achieves high-level regional planning goals developed through participatory stakeholder engagement practices.

Agent-based modeling and simulation of the electricity market with residential demand response

Currently, critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response (RDR) programs. In typical RDR programs, residential customers react to the price or incentive-based signals, but the actions can fall behind flexible market situations. For those residential customers equipped with smart meters, they may contribute more DR loads if they can participate in DR events in a proactive way. In this paper, we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market (DAM). We model and evaluate the interactions between generation companies (GenCos), retailers, residential customers, and the independent system operator (ISO) via an agent-based modeling and simulation (ABMS) approach. The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning (RL) procedure. The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions. Retailers and GenCos optimize their bidding strategies via the RL procedure. The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions. The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker (KKT) conditions. Based on realistic residential data in China, the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households. Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides. The models and methods in this paper may be used by utility companies, electricity retailers, market operators, and policymakers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.

The influence of the structure of technological knowledge on inter-firm R&D collaboration and knowledge discovery: An agent-based simulation approach

This paper studies the impact of the structure of technological knowledge on the collaborative knowledge discovery process. We employ a complex adaptive systems perspective and develop an agent-based model in which firms seek to discover new knowledge by combining previously discovered knowledge. Firms may collaborate with other firms to access a more diverse knowledge base. We experimentally vary the structural properties of the technological knowledge landscape and firm characteristics to study the rate of discovery, propensity to collaborate, and emerging structure of the network of firms. Conceptual interpretations of the simulation results underline the significance of inter-firm R&D collaboration, particularly for specialized firms. We also find that varying the research scope of a firm reveals a trade-off between an individual and the collective perspective, pointing at a systemic failure. Finally, our results provide a complementary explanation for the theoretically desirable cognitive distance between economic actors.

The role of information availability to passengers in public transport disruptions: An agent-based simulation approach

In public transport disruptions, a key relation between the services of operating company and passengers’ satisfaction is the disseminated information. This paper proposes rigorous mathematical descriptions to describe the effects of information availability to passengers, including the user equilibrium and non-equilibrium solutions. The information availability in disruption is summarised in a “who-when-where-what” four-dimensional framework. Based on different information, passengers’ behaviours are assumed and simulated to evaluate the benefits of information availability in public transport disruption. An agent-based micro-simulation model (MATSim) is applied for the city of Zürich, Switzerland, for the benefit of activity-based simulation in a multi-modal network. We use an existing day-to-day replanning method, and extend the study with a within-day replanning approach in MATSim, to study agents’ route choices responding to public transport disruption in one single iteration. The disruption is assumed as a rail track blockage between Zürich HB and Zürich Oerlikon via both Zürich Wipkingen and Zürich Hardbrücke. One benchmark of agents’ behaviours without disruption and other three scenarios based on different information are simulated. Statistic results are analysed for all the agents who may be involved in the defined disruption. Agents’ flow in related transit routes and transport modes shows their adaptations to the corresponding information availability. Agents’ delays and scores reveal that information significantly impacts agents’ satisfaction in public transport disruption.

Agent-based modelling and simulation for ship unloading processes: determining the number of trucks and container cranes

Loading and unloading activities generate nearly fifty per cent of the total cost in port. The loading and unloading process of the container at the port is considered as a complex process since it involves several interrelated components, such as ships, cranes, and trucks. The uncertainty of these component activities might impact the loading and unloading time and cost. Agent-based modelling and simulation (ABMS) approach is a method for analyzing and modelling a complex system. This study aims to simulate the unloading process to determine a strategy to reduces unloading process time in the largest port in Indonesian using ABMS approach. The results show that the agent-based simulation approach is feasible to be applied in port activities. This approach can assist decision-makers in predicting the number of facilities that must be used to minimize processing time.

How the issue time of information affects passengers in public transport disruptions: an agent-based simulation approach

Abstract In public transport disruptions, a key relation between the services of operating company and passengers’ satisfaction is the disseminated information. This paper focus on how the issue time of information affects passengers in the unplanned/unexpected public transport disruptions. Due to different information issue time, passengers’ behaviours are assumed and simulated. An agent-based micro-simulation model (MATSim) is applied for the city of Zurich, Switzerland, for the benefit of heterogeneous activity-based simulation in a multi-modal network. We develop a novel within-day replanning approach in MATSim, to study agents’ route choices responding to public transport disruptions in one single iteration. The disruption is assumed as a rail track blockage between Zurich HB and Zurich Oerlikon via both Zurich Wipkingen and Zurich Hardbrucke. Statistic results are analysed for all the agents who may be involved in the defined disruption. Agents’ travel chain shows their adaptations to the corresponding information scenarios. Agents’ scores reveal that the issue time of information significantly impacts agents’ satisfaction in public transport disruption. In other terms, an investment in better information might be complementary and comparable in an investment in a better management of the operations during the disruption, in interconnected multimodal networks.

Highlighting the benefits of Industry 4.0 for production: an agent-based simulation approach

Abstract: Advanced Manufacturing or Industry 4.0 concepts bring new advances and challenges to current industrial processes. Such concepts are not always well understood and their results in terms of production performance may not be clear. This work proposes a comparison between a traditional manufacturing process and an advanced manufacturing process, both modelled by a multiagent society. In the traditional manufacturing simulation, the agents follow the defined times of each process, including the maintenance times. In the advanced manufacturing simulation, the decision about when to stop a piece of equipment for maintenance is defined by the agent according to data received from sensors and the definitions of the process. The results indicate a significant improvement in equipment usage and consequently higher production in the same time interval. The process simulation clearly indicates that the application of advanced manufacturing concepts in industry is relevant in order to increase the efficiency of production processes. Among the main concepts introduced in advanced manufacturing models are the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Artificial Intelligence (AI). The models generated are computationally simulated using an agent-based simulation method from the software AnyLogic. The results obtained should contribute to encouraging small and medium sized enterprises to adopt the concepts of Industry 4.0 in their businesses.

An Agent-Based Simulation Approach to Model Hospital Logistics

The increasing rate of hospital admissions has resulted in a commensurate increase in the number of treatments and surgeries performed, as well as resource and material usage, and requires planners ...

Simulation of broad front bird migration across Western Europe

The spatial and temporal patterns of broad front bird migration are governed by the geospatial distributions of landmasses, mountain ranges, and weather conditions. These distributions interact with the birds' innate program during migration and are critical to successful migration. Hence, favourable environmental conditions for migration consequently lead to spatio-temporal concentrations, and the evolution of specific migratory flyways. Based on the aerodynamic properties of the ten most abundant nocturnal long-distance migrants, we developed a computational framework to simulate millions of individual trajectories across Europe using an agent-based simulation approach. We simulated a three-week period of autumn migration with a temporal resolution of 30 s. Departure conditions were derived from bird densities observed within the first two hours after sunset, which were extracted from the weather radar network. Individual itineraries were strongly influenced by the behavioural reactions to the environment such as the wind flow, coastlines and mountain ranges. Wind speed and direction are among the key factors that shape migration patterns such as reverse movements observed within some nights. Accumulation of migration was triggered by the combined effect of geographic barriers (coastlines, mountain ranges) and wind. The overall result of the simulation corresponds well with the large-scale pattern of bird migration intensities measured across the study area, for instance the high migration intensity observed between the Atlantic coast and the Pyrenees. Our model framework conjoins all important domains, such as the birds’ preconditions and behavioural scope, as well as the spatio-temporal dynamics of the environment. However, for the time being we cannot decide whether local discrepancies between model and data are due to environmental effects that are not yet captured by our simulation (i.e. effect of rain on migration intensity), or caused by differences in the sensitivity of the local weather radar systems. Due to the limited time period and lack of more accurate data for validation, our findings are preliminary. Further progress in data quality and hopefully better access of bird profiles from the continent-wide weather radar network will allow to improve model performance and implement a bird migration forecasting similar to weather forecasts. Apart from assessing conflicts between human activities and bird movements, this would greatly enhance our understanding of biomass flow on a large scale.

Modeling the impacts of policy measures on resident's PM2.5 reduction behavior: an agent-based simulation analysis.

With the rapid economic growth of China, the increasingly serious environmental problems of haze pollution have become a large concern. Urban resident's PM2.5 reduction behavior contributes significantly to Chinese haze pollution control. Resident-level policy measures are beneficial for encouraging residents to engage in PM2.5 reduction behaviors. The current research aims to explore the long-term intervention effects of three types of policies (i.e., command and control policies, economic incentive policies and education-guided policies) on resident's PM2.5 reduction intention and actual behavior. Based on the agent-based modeling and simulation approach, a resident's PM2.5 reduction behavioral simulation model is developed, and data adopted from a questionnaire survey are analyzed. The simulation results show that resident's PM2.5 reduction intention is motivated by the interactions among resident agents, and it eventually stabilizes at a higher level (from 4.11 to 4.48). Moreover, the effects of the three types of policy measures on PM2.5 reduction behavior vary depending on the specific scenarios. With respect to single-policy scenarios, these policies all enhance the actual resident's PM2.5 reduction behavior over the long term. The effects of command and control policies (M = 3.42) and education-guided policies (M = 3.44) are much better than those of the economic incentive policies (M = 3.15). Regarding policy combination scenarios, a combination of economic incentive policies and education-guided policies (MII = 4.15) has a remarkable promotional effect over others for encouraging residents to conduct PM2.5 reduction behaviors. Based on the results, implications and suggestions for improving current resident-level PM2.5 reduction policies and encouraging resident's PM2.5 reduction behavior are provided.

Using agent-based modelling for investigating modal shift: The case of university travel

Travel mode choices are a result of several factors and how they affect individual travellers. This paper examines those factors influencing travellers’ mode choices commuting to and from a university. Furthermore, we investigate how a shift to alternative modes can be stimulated within the current transport system environment of that university. From focus groups studies, seven measurable themes were identified as metrics for travellers’ satisfaction. Descriptive data has been collected from 348 participants through questionnaires. The analysis of the questionnaires provided insights into the development of appropriate policies to stimulate travellers’ mode shift. To allow for studying the impact of applying proposed interventions over time, we simulated the effects of those interventions on travellers’ mode choice by using an agent-based social simulation approach. We employed a framework designed for modelling modal shift in the transport domain to build the simulation model, taking the themes into consideration. The outcomes of the study assisted in understanding how decision factors and their interconnections contribute to sub-populations of travellers’ choice. In addition, our experiments helped in assessing the importance of interactions among travellers on their decision making. Such an understanding provides insight into those factors within the system that need to be considered when policymakers develop strategies for interventions for mode shift. The outcomes of the simulation experiments indicate that different policy interventions result in distinct travellers’ mode adoption patterns and that interventions perform better when the right categories or groups of travellers are targeted. In addition, the intervention should focus on the right travellers’ concerns and be applied in the right proportion. This social simulation study has also demonstrated how a theory-based framework can be used with survey data in numerical experiments to explore real-life scenarios for the development of actions to promote behavioural changes.

The Brexit Game: Uncertainty and Location Decision

This article aims to analyze the strategic decision about firms location choice faced with the uncertainty surrounding Brexit. We combine Evolutionary Game Theory (EGT) and spatial Agent-based Simulation (ABS) approaches with input-output analysis. Firms are competing in two different sectors: (a) crop and animal production; (b) financial service activities. We separate the European Union in manifold regions and consider the following factors in the decision making: (i) market potential; (ii) productive integration; (iii) labor costs and (iv) displacement cost. Firms assign weights to each of these factors. Our results suggest that in traditional sectors firms tend to seek unsaturated markets. In sectors related to services, the greater the uncertainty, the greater the likelihood that firms will move.

Agent-based modelling and heuristic approach for solving complex OEM flow-shop productions under customer disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction.In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches.

Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework

We develop a general-purpose agent-based simulation and modeling approach to analyze how user–recommender interactions affect recommender systems in the long run. Our explorations show that, over time, user–recommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on recommendations, while helping users discover relevant items, actually hurts the future diversity of items that are recommended and consumed as well as slows down the system’s learning pace (i.e., the rate of predictive accuracy improvement). We also demonstrate unique benefits of certain hybrid consumption strategies—that is, that take advantage of both popularity- and personalization-based recommendations—in facilitating improvements in consumption relevance over time. Because users’ consumption strategies can significantly influence the longitudinal performance of recommender systems, it is important for designers to analyze the histories of a system’s recommendations and users’ choices to infer and understand users’ consumption strategies. This would enable the system to anticipate users’ consumption behavior and strategically adjust the system’s parameters according to its long-term performance objectives.

A modeling framework for the application of multi-paradigm simulation methods

Decisions about modeling and simulation (M&S) of real-world systems need to be evaluated prior to implementation. Discrete Event, System Dynamics, and Agent Based are three different modeling and simulation approaches widely applied to enhance decision-making of M&S of these systems. Combining and/or integrating these methods can provide solutions to a plethora of systems’ problems. However, current solutions and frameworks do not provide guidance for selecting and deploying M&S models. Hence, the aim of this work is to present a generic modeling framework for combining and/or integrating Discrete Event, System Dynamics, and Agent Based simulation approaches. The framework is termed multi-paradigm modeling framework (MPMF). In this paper, we describe the research methodology that was followed for the development of MPMF, the different phases of MPMF, and the generic relationships of forming and deploying multi-paradigm simulation models. Then we evaluate the framework by using it for the implementation of a universal task analysis simulation model (UTASiMo). The MPMF provided guidance on what methods need to be incorporated into the UTASiMo models, what information is exchanged among those models, and how these models are connected and interact with each other.

AbSRiM: An Agent-Based Security Risk Management Approach for Airport Operations.

Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent‐based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent‐based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent‐based modeling and Monte Carlo simulation at its core. Agent‐based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks.The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.

Effects of passenger characteristics and terminal layout on airport retail revenue: an agent-based simulation approach

ABSTRACTThis paper examines the effect of passenger characteristics and terminal layout on airport retail revenue using an agent-based simulation approach. Simulation results show that passenger mix (that is, the mix of shopper types according to a typology of airport shoppers) has a profound effect on airport retail revenue; the larger the number of ‘shopping lovers’ there are among passengers, the higher the airport retail revenue. Results also reveal that group travel can lead to negative effects on retail in certain terminal layouts, and that the amount of free dwell time that a passenger has can affect spending due to less retail engagement. This paper shows a combined effect of passenger characteristics and terminal layout on airport retail revenue, and discusses the implications of these results for future airport terminal design that aims to maximise retail potential.

Identifying housing market expectation transformation: an agent-based housing market testbed

Price variation is complex in the market. Expectation transformation plays a key role in the variation process. Incomplete and imperfect information are involved in the process forming expectation transformation. This paper tries to identify expectation transformation under different exogenous conditions in a housing market through an agent-based simulation approach. Firstly, this paper constructs basic model elements according to empirical sense and theoretical findings. Secondly, this paper builds an intelligence-and-trading behaviour model of agents based on genetic programming (GP) approach. The agent-based housing market testbed (ABHMT) is constructed. To verify the model, this paper sets two groups of control experiments. Through the experiments, we reveal five new findings in the field. The control experiments show the effectiveness of ABHMT. The comparison of real-life history scenarios and the simulation results shows the validity of the model. With the help of ABHMT, it is possible to simulate reality-alike situations to assist more market analyses for a certain application case.

Identifying housing market expectation transformation: an agent-based housing market testbed

Price variation is complex in the market. Expectation transformation plays a key role in the variation process. Incomplete and imperfect information are involved in the process forming expectation transformation. This paper tries to identify expectation transformation under different exogenous conditions in a housing market through an agent-based simulation approach. Firstly, this paper constructs basic model elements according to empirical sense and theoretical findings. Secondly, this paper builds an intelligence-and-trading behaviour model of agents based on genetic programming (GP) approach. The agent-based housing market testbed (ABHMT) is constructed. To verify the model, this paper sets two groups of control experiments. Through the experiments, we reveal five new findings in the field. The control experiments show the effectiveness of ABHMT. The comparison of real-life history scenarios and the simulation results shows the validity of the model. With the help of ABHMT, it is possible to simulate reality-alike situations to assist more market analyses for a certain application case.

Exploring the Economic, Environmental, and Travel Implications of Changes in Parking Choices due to Driverless Vehicles: An Agent-Based Simulation Approach

AbstractFully driverless automated vehicles (AVs) could considerably alter the proximity value of parking, due to an AV’s ability to drop passengers off at their destination, search for cheaper par...