Multi-agent Modeling Approach Research Articles

Modeling and simulation of technological innovation diffusion of high-performance construction materials in transportation engineering

This study focuses on the diffusion process of technological innovations in high-performance construction materials (HPCMs) within the field of transportation engineering. The aim is to explore the impact of various factors on this process through modeling and simulation. The research employs a multi-agent modeling approach to construct a simulation model of the diffusion of technological innovations of HPCMs among firms and owners. It analyzes the impact of firms’ strategy choices, knowledge sharing, and government policies on the diffusion of technological innovations of HPCMs. Through simulation experiments, this study reveals the impact of the proportion of firms choosing followership or innovation strategies on the efficiency of technology diffusion within complex networks, as well as the double-edged effect of knowledge sharing. Additionally, the findings indicate that adjustments of government policies at different stages of diffusion have a significant impact on market dynamics. This study provides theoretical foundations and practical recommendations for promoting the research and application of high-performance construction materials, thereby advancing green and sustainable development in transportation engineering.

Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-vehicle-road system at the mesoscale level, integrating the characteristics of heterogeneous driving behavior. We developed a traffic flow simulation model for a three-lane urban arterial road segment and used rule-based algorithms to simulate vehicle following, lane changing, and collision behavior. A visualization simulation program was developed using NetLogo software to observe and analyze vehicle behavior. The findings revealed that collision events were the leading cause of traffic bottlenecks, impacting the continuity and coherence of traffic flow. Even after the collision event subsided and congestion eased, the average vehicle speed could not fully recover to its original level, reaching only about 70% of the maximum vehicle speed. Aggressive drivers exhibited distinct speed control strategies compared to conservative and ordinary drivers. This study demonstrates the effectiveness of multi-agent modeling in capturing the relationship between traffic bottlenecks and collision events, highlighting the influence of dynamic traffic events on driving behavior.

Assessing the impact of isolation policies on epidemic dynamics through swarm entropy.

Isolation policies are an effective measure in epidemiological models for the prediction and prevention of infectious diseases. In this paper, we use a multi-agent modeling approach to construct an infectious disease model that considers the influence of isolation policies. The model analyzes the impact of isolation policies on various stages of epidemic from two perspectives: the external environment and agents behavior. It utilizes multiple variables to simulate the extent to which isolation policies influence the spread of the pandemic. Empirical evidence indicates that the progression of the epidemic is primarily driven by factors such as public willingness and regulatory intensity. The improved model, in comparison to traditional infectious disease models, offers greater flexibility and accuracy, addressing the need for frequent modifications in fundamental models within complex environments. Meanwhile, we introduce "swarm entropy" to evaluate infection intensity under various policies. By linking isolation policies with swarm entropy, considering population structure, we quantify the effectiveness of these isolation measures. It provides a novel approach for complex population simulations. These findings have facilitated the enhancement of control strategies and provided decision-makers with guidance in combating the transmission of infectious diseases.

Simulating staff activities in healthcare environments: An empirical multi-agent modeling approach

The layout of the healthcare environment profoundly impacts healthcare efficiency, safety, employee satisfaction, and the sustainability of operations, mainly based on occupant behavior analysis. However, due to the increase in professionalism and interprofessional collaboration among healthcare practitioners and the complexity of the physical environment, existing research tools face challenges in describing and analyzing diverse occupant behaviors. This study aims to offer an empirically grounded multi-agent modeling approach as a research tool to simulate and reproduce occupant behavior within healthcare settings. Employing a design science research method, the proposed approach encompasses four stages: empirical data collection, development of behavioral rules, agent-based modeling, and validation. A continuous 24-h observational study involving doctors, nurses, and auxiliary staff within a trauma intensive care unit is conducted to demonstrate the development and application process. Results show that the proposed modeling approach achieves a simulation deviation of 8.31 % in individual walking distances and a 16.26 % deviation in overall staff spatial visitation frequency compared to real-world observations. We believe that using simple if-then rules to represent events and probability rules to describe other basic activities can simulate complex staff activities in a healthcare environment. Interdisciplinary expert evaluation acknowledged the utility of this approach. This study offers a comprehensive dataset of behaviors and contributes methodologically to evaluating the efficiency of built environment layouts. The innovation in this study lies in its scalability, extending from events and individuals to encompass the all staff in levels of modeling and validation, ensuring a consistent alignment with empirical data at each stage.

Ultimately Bounded TSK Fuzzy Management in Urban Traffic Stream Mechanism: Multi-Agent Modeling Approach

In this paper, a novel control approach based on the selection of the type of traffic light and the period of the green phase to accomplish an optimum balance at intersections is proposed. This balance should be flexible to the static behavior of time, and randomness in a traffic situation, which the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control over the crash of cars. The proposed method was specifically investigated at the intersection through an appropriate timing of traffic lights by sampling a multi-agent system. It consists of a large numbers of intersections, each of which is considered as an independent agent that exchanges information with each other, and the stability of each agent is provided separately. The robustness against uncertainties, scalability, and stability of the closed-loop overall system are the main merits of the proposed methodology. The simulation results show that the fuzzy intelligent controller in this multi-factor system which is a TS fuzzy is more useful than scheduling in the fixed-time method and it reduces the lengths of vehicles queuing.

A Proposal of Multi-agent Modeling Approach in SPS Line Simulation Considering Human Centered Design

A Proposal of Multi-agent Modeling Approach in SPS Line Simulation Considering Human Centered Design

Uncovering Stakeholders’ Participation to Better Understand Land Use Change Using Multi-Agent Modeling Approach: An Example of the Coal Mining Area of Shanxi, China

Recent decades have witnessed increasing human disruption and the acceleration of fragile natural habitats, especially in coal mining areas in developing countries or regions, which will inevitably lead to sharp land use and cover change (LUCC). Most LUCC models focus more on the research of “land” than “human” in human-land coupled systems, while the simulation and prediction of LUCC involving stakeholders are still deficient. Aiming to reveal the shaping process of LUCC through the stakeholders’ participation, we conducted an in-depth interview in a typical coal mining area of Shanxi, China, and developed an agent-based model by quantifying the stakeholders’ land-use decision-making rules to simulate and predict land use change in different scenarios. The analysis illustrated that the stakeholders’ participation in different periods had shaped the land use pattern in the coal mine area. The area of cultivated land has decreased from 272.34 hm2 to 118.89 hm2, while industrial and mining land increased dramatically by 78.66 hm2 from 2012 to 2019. The attitude and willingness of farmers towards land use varied greatly and were affected by livelihood capital. Part-time farmers whose agricultural income only accounted for 20–80% of the total income were in favor of farmland requisition by coal mining enterprises compared to full-time farmers. We quantified the rules between the attributes of the stakeholders at the micro level and land use changes at the macro level and proposed the multi-agent simulation model, which was effective and verified by a fitting test where the overall accuracy of the Kappa coefficient was 0.83 and could be used to predict future LUCC in research areas by setting the decision parameters in specific scenarios. These outcomes provided a scientific reference for landscape simulation and the prediction of a human-land coupling system while promoting the effectiveness of spatial planning policies.

Assessment Model for Rapid Suppression of SARS-CoV-2 Transmission under Government Control.

The rapid suppression of SARS-CoV-2 transmission remains a priority for maintaining public health security throughout the world, and the agile adjustment of government prevention and control strategies according to the spread of the epidemic is crucial for controlling the spread of the epidemic. Thus, in this study, a multi-agent modeling approach was developed for constructing an assessment model for the rapid suppression of SARS-CoV-2 transmission under government control. Different from previous mathematical models, this model combines computer technology and geographic information system to abstract human beings in different states into micro-agents with self-control and independent decision-making ability; defines the rules of agent behavior and interaction; and describes the mobility, heterogeneity, contact behavior patterns, and dynamic interactive feedback mechanism of space environment. The real geospatial and social environment in Taiyuan was considered as a case study. In the implemented model, the government agent could adjust the response level and prevention and control policies for major public health emergencies in real time according to the development of the epidemic, and different intervention strategies were provided to improve disease control methods in the simulation experiment. The simulation results demonstrate that the proposed model is widely applicable, and it can not only judge the effectiveness of intervention measures in time but also analyze the virus transmission status in complex urban systems and its change trend under different intervention measures, thereby providing scientific guidance to support urban public health safety.

Multiagent modeling of pedestrian-vehicle conflicts using Adversarial Inverse Reinforcement Learning

There is a need for a better understanding of the collision avoidance behavior of road users in near misses. Recently, several models of road user behavior in near misses have been proposed. However, despite the multiagent nature of road user interactions, most of these studies modeled their behavior using a single-agent approach. However, this approach is unrealistic and can limit the models’ accuracy. Therefore, this study proposes the Markov-Game (MG) framework for modeling pedestrian-vehicle interactions and their collision avoidance mechanisms. Pedestrian-vehicle conflicts in a mixed traffic environment in China are extracted using computer-vision algorithms. Pedestrian and vehicle reward functions are recovered via the Multiagent Adversarial Inverse-Reinforcement-Learning approach. Road user optimal policies and collision avoidance mechanisms are predicted using multiagent Actor-Critic deep-reinforcement-learning. The results demonstrate the superiority of the multiagent modeling approach in predicting road user behavior, their collision avoidance mechanisms, and the Post-Encroachment-Time (PET) compared to a baseline single-agent model.

Game Theory-Based Modeling of Multi-Vehicle/Multi-Pedestrian Interaction at Unsignalized Crosswalks

<div class="section abstract"><div class="htmlview paragraph">The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent. During the decision making process, each pedestrian takes into account approaching vehicles and their expected future behavior with respect to all pedestrians (i.e., himself/herself and other pedestrians), and vice versa for vehicles. This generalized multi-agent model is conceived to support simulation scenarios concerning multi-lane roads and pedestrians approaching from both sides of a road, as well. The model is verified by means of large-scale simulations and various scenarios.</div></div>

A coupled human–natural system analysis of freshwater security under climate and population change

Limited water availability, population growth, and climate change have resulted in freshwater crises in many countries. Jordan's situation is emblematic, compounded by conflict-induced population shocks. Integrating knowledge across hydrology, climatology, agriculture, political science, geography, and economics, we present the Jordan Water Model, a nationwide coupled human-natural-engineered systems model that is used to evaluate Jordan's freshwater security under climate and socioeconomic changes. The complex systems model simulates the trajectory of Jordan's water system, representing dynamic interactions between a hierarchy of actors and the natural and engineered water environment. A multiagent modeling approach enables the quantification of impacts at the level of thousands of representative agents across sectors, allowing for the evaluation of both systemwide and distributional outcomes translated into a suite of water-security metrics (vulnerability, equity, shortage duration, and economic well-being). Model results indicate severe, potentially destabilizing, declines in freshwater security. Per capita water availability decreases by approximately 50% by the end of the century. Without intervening measures, >90% of the low-income household population experiences critical insecurity by the end of the century, receiving <40 L per capita per day. Widening disparity in freshwater use, lengthening shortage durations, and declining economic welfare are prevalent across narratives. To gain a foothold on its freshwater future, Jordan must enact a sweeping portfolio of ambitious interventions that include large-scale desalinization and comprehensive water sector reform, with model results revealing exponential improvements in water security through the coordination of supply- and demand-side measures.

A state-of-the-art review of multi-agent modelling of crowd dynamic

This paper reviews the multi-agent modelling (MAM) which applied as a platform to simulate the interaction of crowd dynamics. The research and development of MAM related to crowd dynamics can generate specific analytical evaluation, improve the safety level of the pedestrians and the quality operation of the urban space. Furthermore, crowd dynamics have become a major concern for urban space and are mainly constrained imposed by the ever-increasing populations. Consequently, these scenarios causing more attention are paid to the crowd safety in capacity assessment, especially during an emergency evacuation. This paper presents a concise review on the development of MAM over the past few years. The discussion on the state of the art of the multi-agent methods includes the history of the multi-agent method and the development of multi-agent modelling. This paper also discussed the state of the art of multi-agent methods in modelling crowd dynamic by focusing only on the microscopic approach. The advantages of multi-agent modelling approach have become one of the best methodological approaches in analysing and predicting crowd performance in the real world as a substitute for physical experimentation in a current research field.

Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles

This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions.

Optimization of Micro-Grid Electricity Market Based on Multi Agent Modeling Approach

Micro-grids are the key technologies known to solve challenges such as increased electric demand, fatigue electric installations, electrical leakage and pressures and opposition from environmental advocacy groups. The current article is presenting an improved optimization algorithm based on a differential evolution algorithm to achieve the optimal response for managing distributed energy resources in micro-grids. The simulation results show that: 1) The final cost of network management in systems based on the agent is very favorable compared to a network regardless of the agent and also are economically much more useful and effective in coordinating various energy sources. 2) The results of the proposed algorithm are much better in comparison with the results of the Fireflies optimization algorithm, a differential evolution algorithm and the particle swarm algorithm. This comparison proves the high performance of the algorithm.

Multi-agent modelling approach for evaluating the city logistics dynamic in a vulnerability situation: An exploratory study in Belo Horizonte (Brazil)

Abstract This paper presents the results of a multi-agent modelling approach for evaluating urban goods distribution in a vulnerability situation. We analyse the impact of floods on urban freight transport. The model is based on stakeholders’ relationships and behaviours and the results are presented with regard to the environmental and financial impacts. The consistency of the theoretical and analytical routines used was checked with the simulation results. The results showed the benefits of using multi-agent modelling analysis to reduce impacts on urban freight transport in flooding situations.

Evaluation of Urban Distribution Centers Using Multiagent Modeling with Geographic Information Systems

Current barriers to maintaining economies of scale for delivery include far distances of the depots from customers, road usage restrictions, customers' delivery requests, and social, environmental, and economic costs. In many developed and developing countries, the effects of urbanization have increased city population. Such migration to cities is also attracting more movement of commodities into the city center. This research aimed to study the effectiveness and viability of urban distribution centers from a tactical viewpoint of planning for city logistics. A multiagent modeling approach with geographical information system support was used to test an urban distribution center (UDC) in Osaka City, Japan. The key findings from this research concluded that the UDC has potential for emissions reduction, but sustainability of the UDC is reliant on the UDC charges and carriers' sensitivity to the rate of charges.

Simulation-based Performance Analysis of a Miniload Multishuttle Order Picking System

In today's fluctuating business environment, flexibility, responsiveness, and reconfigurability in the field of warehousing systems are key characteristics as well as the level of automation, cost effectiveness and maximum throughput. The miniload multishuttle system represents relatively a new scalable automated storage and retrieval solution which provides considerable flexibility for adapting throughput capacity to meet market needs. The miniload multishuttle system comprises autonomous vehicles (shuttles), lifts, and a system of rails that facilitate movement of the vehicles in the x and y dimensions in a tier. In this new technology, the shuttles can be moved between different tiers by means of shuttle lifts. Especially in large and complex material flow systems, the modeling problem arises with the interactions between transactions and the collisions between shuttles. An agent-based simulation approach that differs from discrete-event simulation (DES) offers an alternative way to model the autonomous control of the multishuttle system. This research develops a simulation model for the miniload multishuttle order picking system using the multi-agent modeling approach. The main objective of this paper is to create a detailed simulation model and to evaluate the performance of the system in order to support in design process of miniload multishuttle order picking systems.

A Semiotic Multi-Agent Modeling Approach for Clinical Pathway Management

Clinical pathway (CP) is the standard way to manage medical activities in order to improve the efficiency and minimize the cost of healthcare service. However current application of CPs can hardly adapt to the dynamic and complex clinical processes which involve the coordination of social agents in hospital. This paper focuses on developing a multi-agent modeling method to build up the system that can increase the execution efficiency of clinical pathway. In particular, we present a semiotic approach to analyze the requirement of clinical pathway. Furthermore we describe how the EDA (Epistemic, Deontic, and Axiological) agent and the negotiation model are realized and applied to support the clinical pathway management. Finally, the clinical pathway of breast cancer is presented to demonstrate our methods

Modelling of spatial dynamics and biodiversity conservation on Lure mountain (France)

Our study is part of a multi-disciplinary research project aimed at stimulating debate among researchers and local managers. The central question of this multi-disciplinary research project was to better understand and manage high biodiversity value open habitats threatened with shrub encroachment and landscape closure, a common problem throughout Europe. Here, we study shrub encroachment and its impact on biodiversity conservation in Mount Ventoux, a MAB Biosphere Reserve located at the southernmost tip of the French Alps. We show how using a multi-agent modelling approach provide a valuable framework to confront two potentially conflicting conservation efforts in this mountain Mediterranean landscape, that of the within-species diversity of a tree ( Abies alba, the European silver fir) and that of an endangered species ( Vipera ursinii ursinii, the Orsini viper). A companion modelling approach – approach which aims at transmitting and sharing knowledge, methods and tools that help understand and strengthen the collective decision making process of stakeholders sharing a common resource – was used in order to collectively represent the main activities underway on the mountain and to have a tool to address both open landscapes rehabilitation and restoration of forest environments. The co-construction of the model allowed us to build a shared representation of the territory under study and to develop and compare alternative management scenarios with local stakeholders, both to evaluate their impact on biodiversity and to provide information for forest and grazing management practice.

Multi-Agent Modeling in Managing Six Sigma Projects

In this paper, a multi-agent model is proposed for considering the human resources factor in decision making in relation to the six sigma project. The proposed multi-agent system is expected to increase the acccuracy of project prioritization and to stabilize the human resources service level. A simulation of the proposed multi-agent model is conducted. The results show that a multi-agent model which takes into consideration human resources when making decisions about project selection and project team formation is important in enabling efficient and effective project management. The multi-agent modeling approach provides an alternative approach for improving communication and the autonomy of six sigma projects in business organizations.