Application Of Agent-based Simulation Research Articles

Analysis of the methods for accounting the renewable and non-renewable resources in scheduling

The paper discusses a scheduling problem taking into account the constraints on the renewable and non-renewable resources and availability of the works due dates. For the problem, an overview of the scheduling methods is given. The considered scheduling methods include approximate optimization algorithms and hybrid algorithms based on the genetic algorithm. It is concluded that it is necessary to use heuristic methods to find a solution. A new hybrid scheduling method based on multi-agent simulation is proposed. Application of agent-based simulation allows one to take into account the life cycle of the non-renewable resources and to distribute the renewable resources during scheduling. The genetic algorithm ensures search for the effective schedule with aim at the minimum cost of attracting the additional renewable resources in order to maintain the works deadlines.

Designing a benchmark for the performance evaluation of agent-based simulation applications on HPC

Agent-based modeling and simulation (ABMS) is a class of computational models for simulating the actions and interactions of autonomous agents with the goal of assessing their effects on a system as a whole. Several frameworks for generating parallel ABMS applications have been developed taking advantage of their common characteristics, but there is a lack of a general benchmark for comparing the performance of the generated applications. We propose and design a benchmark that takes into consideration the most common characteristics of this type of applications and includes parameters for influencing their relevant performance aspects. We provide an initial implementation of the benchmark for FLAME, FLAME GPU, Repast HPC and EcoLab, some of the most popular parallel ABMS platforms, and use it for comparing the applications generated by these platforms. The obtained results are mostly in agreement with previous studies, but the designed and implemented specification has allowed for testing a wider set of aspects, such as the number of interacting agents, the amount of interchanged data or the evolution of the workload and obtaining more reliable results.

SOLVING THE DIAL-A-RIDE PROBLEM USING AGENT- BASED SIMULATION

The ‘dial-a-ride problem’ (DARP) requires a set of customers to be transported by a limited fleet of vehicles between unique origins and destinations under several service constraints, including within defined time windows. The problem is considered NP-hard, and has typically been solved using metaheuristic methods. An agent-based simulation (ABS) model was developed, where each vehicle bids to service customers based on a weighted objective function that considers the cost to service the customer and the time quality of the service that would be achieved. The approach applied a pre- processing technique to reduce the search space, given the service time window constraints. Tests of the model showed significantly better customer transit and waiting times than the benchmark datasets. The ABS was able to obtain solutions for much larger problem sizes than the benchmark solutions, with this work being the first known application of ABS to the DARP.

Impact of road environment on drivers’ behaviors in dilemma zone: Application of agent-based simulation

At a signalized intersection, there exists an area where drivers become indecisive as to either stop their car or proceed through when the traffic signal turns yellow. This point, called a dilemma zone, has remained a safety concern for drivers due to the great possibility of a rear-end or right-angle crash occurring. In order to reduce the risk of car crashes at the dilemma zone, Institute of Transportation Engineers (ITE) recommended a dilemma zone model. The model, however, fails to provide precise calculations on the decision of drivers because it disregards the supplemental roadway information, such as whether a red light camera is present. Hence, the goal of this study was to incorporate such roadway environmental factors into a more realistic driver decision-making model for the dilemma zone. A driving simulator was used to determine the influence of roadway conditions on decision-making of real drivers. Following data collection, each driver's decision outcomes were implemented in an Agent-Based Simulation (ABS) so as to analyze behaviors under realistic road environments. The experimental results revealed that the proposed dilemma zone model was able to accurately predict the decisions of drivers. Specifically, the model confirmed the findings from the driving simulator study that the changes in the roadway environment reduced the number of red light violations at an intersection.

Agent-based simulation applications in marketing research: an integrated review

Agent-based simulation has been used to study a wide variety of complex and social systems. This paper presents the results of a comprehensive and multi-dimensional analysis of the application of agent-based modelling and simulation in marketing. Papers are evaluated with respect to types of interaction and communication channels, individual cognitive decision-making styles, social system structures, application area, goals of the simulation study, and other model properties. Furthermore, network analysis and text-mining methods are employed as two novel approaches to establish the current state of the literature and reveal literature trends, gaps, and strengths. Various emergent phenomena and important findings from the application of agent-based simulation in different areas within marketing are also discussed. Finally, the results of the literature analysis are used to make future research recommendations by providing an architecture for conceptual model design and general guidelines to increase the credibility of agent-based models in the marketing domain.

Impact of Enhanced Cognitive Capacity on the Performance of Disaster Response Governance: An Application of Agent-Based Simulation

Impact of Enhanced Cognitive Capacity on the Performance of Disaster Response Governance: An Application of Agent-Based Simulation

A systematic approach for new service concept generation: Application of agent-based simulation

In new service development (NSD), a concept design that influences the direction of other activities is considered as a crucial stage. Although several methods exist, most of them cannot reflect responses of future potential customers because they assume that customers have the same responses in the future. Therefore, by using agent-based simulation (ABS) that can understand the future status of a service system, this paper suggests a new approach to creating the most promising service concept. The proposed approach considers the future trends of various customer responses and service concept strategies of firms by using ABS, generating a more accurate concept. By applying the suggested approach to a healthcare industry, its practicability and utility are verified. The novel approach based on the agent-based simulation method, which has customer, service provider, and competitor agents, is expected to be useful for service providers in a competitive market.

Assessing Walkability in the City of Buffalo: Application of Agent-Based Simulation

Significant research has been conducted on how environmental attributes influence people’s decisions to walk. In much of this research, however, environmental attributes are averaged for neighborhoods or census geographies for sampled populations. Moreover, the effect of an agent’s walking choices on other actors is not adequately represented by either objective or perceived measures in the literature. Macro-level patterns of walkability arise from interactions across actors and urban environments. The agent-based approach allows for modeling individual uses of the environment by treating the populations as objects that can interact with the environment and other people. This study builds on previous research on pedestrian movement and geographic information system (GIS) measures of the built environment using the agent-based approach to explore the dynamics of the built environment and people’s decision-making processes concerning walking. The results show that models that take individual perspective into account and include social interaction can better capture characteristics of the built and social environment that influence people’s walking choices. This method lays out a new framework for assessing macro-level patterns of walkability across a city using micro-level data.

An application of agent-based simulation to the management of hospital-acquired infection

Hospital patients who are colonised with methicillin-resistant Staphylococcus aureus (MRSA), may transmit the bacteria to other patients. An agent-based simulation is designed to determine how the problem might be managed and the risk of transmission reduced. Most MRSA modelling studies have applied mathematical compartmental models or Monte Carlo simulations. In the agent-based model, each patient is identified on admission as being colonised or not, has a projected length of stay and may be more or less susceptible to colonisation. Patient states represent colonisation, detection, treatment, and location within the ward. MRSA transmission takes place between pairs of individuals in successive time slices. Various interventions designed to reduce MRSA transmission are embedded in the model including: admission and repeat screening tests, shorter test turnaround time, isolation, and decolonisation treatment. These interventions can be systematically evaluated by model experimentation.

Structure and Flexibility of Project Teams under Turbulent Environments: An Application of Agent-Based Simulation

The extent of authority and autonomy granted to the project manager and to individual team members has a significant impact on the ability of the project team to meet stakeholders’ goals and expectations. This is particularly true in dynamic environments, where resources, requirements, and other conditions change rapidly, and a great deal of innovation is required. In this paper, we provide some recommendations regarding the structure and flexibility of project teams operating in turbulent environments. The recommendations are based on a set of experiments carried out with agent-based simulation, a novel tool for organizational design. The observed results show that under high uncertainty, loose designs for project teams outperform tight organizational structures.