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The purpose of this paper is to propose an availability modeling method of complex multiple units system (CMUS) based on the multi-agent technique.

Based on the multi-agent technique, this paper describes the availability model structure for CMUS and develops agent-based models of components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff, as well as the communication method among the different agents. On the basis of the agent-based availability modeling theory, the availability simulation scheme of CMUS is given using MATLAB. Thus, the availability modeling theory of CMUS and its simulation method are developed. To demonstrate the applicability of the proposed availability modeling method, a numerical example is given.

The proposed agent-based modeling method is applicable to availability modeling of CMUS, including the modeling of component failure, maintenance tools/fields/staff, maintenance policy, and structural/economic dependence among components.

As a bottom-top, modular, expandable, and reusable modeling theory, the agent-based modeling method might be useful for availability modeling of different CMUSs in reality.

The multi-agent technique is introduced into availability modeling of multi-component systems in this paper. Thus, it is possible to model failure of many components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff together for availability analysis of complex systems of equipment.

The purpose of this paper is to develop a multi-agent-based model for quantitatively measuring how the design change management strategies improve project performance.

Based on questionnaires and interviews, this paper investigates the coordination mechanism of risks due to design changes in prefabricated construction (PC) projects. Combined with all the variables related with design change risks, a multi-agent-based simulation model is proposed to evaluate the design change management effect.

The coordination mechanism between design change factors, design change events, risk consequence and management strategy in PC projects is described and then the simulation-based design change management mechanism in PC projects is used to assess the effect of management strategies under dynamic scenarios.

PC projects have rapidly increased in recent years due to the advantages of fast construction, high quality and labor savings. Different from traditional on-site construction, the impact and risk from design changes are likely to be greater due to the prefabricated project being multi-stage, highly interactive and complex. The simulations presented in this paper make it possible to test different design change management strategies in order to study their effectiveness and support managerial decision making.

In shield tunneling projects, human, shield machine and underground environment are tightly coupled and interacted. Accidents often occur under dysfunctional interactions among them. Therefore, this paper aims to develop a multi-agent based safety computational experiment system (SCES) and use it to identify the main influential factors of various aspects of human, shield machine and underground environment.

The methods mainly comprised computational experiments and multi-agent technologies. First, a safety model with human-machine-environment interaction consideration is developed through the multi-agent technologies. On this basis, SCES is implemented. Then computational experiments are designed and performed on SCES for analyzing safety performance and identifying the main influential factors.

The main influential factors of two common accidents are identified. For surface settlement, the main influential factors are ranked as experience, soil density, soil cohesion, screw conveyor speed and thrust force in descending order of influence levels; for mud cake on cutter, they are ranked as soil cohesion, experience, cutter speed and screw conveyor speed. These results are consistent with intuition and previous studies and demonstrate the applicability of SCES.

The proposed SCES provides comprehensive risk factor identification for shield tunneling projects and also insights to support informed decisions for safety management.

A safety model with human-machine-environment interaction consideration is developed and computational experiments are used to analyze the safety performance. The novel method and model could contribute to system-based safety research and promote systematic understanding of the safety performance of shield tunneling projects.

The purpose of this paper is to develop a multi-agent-based simulation model for the online opinion dissemination during hazardous chemical leakage emergencies into rivers in China, to explore an appropriate crisis information release policy of China’s government for controlling public panic.

In the proposed model, two fundamental attributes of crisis information, i.e., truthfulness (for true or false news) and attitude (for positive, neutral or negative opinion), are considered. Four major agents in the online community system, i.e., citizens, the government, media and opinion leaders, are included. Using four typical accidents of hazardous chemical leakage into rivers in China as case studies, insightful policy implications can be obtained for crisis management and panic control.

The news about the terrible potential damages from such a type of accidents will instantly arise wide-ranging public panic; therefore, the corresponding crisis information release policy should be carefully designed. It is strongly advised against publishing false news to temporarily conceal the accidents, which will seriously hurt the government’s reputation and agitate much larger-scale public panic in terms of degree and duration. To mitigate public panic, the true news especially about treatment measurements should be published immediately. If the government does nothing and releases no crisis information, the public panic will go out of control.

This paper only focuses on the crisis information release policies from the perspectives of the government. Furthermore, this study especially focuses on the cases in China, and extending the proposed model study for general contexts is an important direction to improve this study. Finally, the proposed model should be extended to other types of emergencies to further justify its generalization and universality, especially various natural catastrophes like storms, floods, tsunamis, etc.

This paper develops a multi-agent-based model for online public opinion dissemination in emergency to explore an appropriate crisis information release policy for controlling public panic stemming from hazardous chemicals leakage accidents into rivers. The proposed model makes major contributions to the literature from two perspectives. First, the crisis information about emergency accidents are divided into true and false news based on the truthfulness attribute, and into neutral, positive and negative emotions based on the attitude attribute. Second, the proposed model covers the main agents in the online virtual community.

Knowledge discovery in databases (KDD) is a tedious and repetitive process. A challenge for the effective use of KDD is understanding and confirming its results derived from the harmonized process. To exploit the advantages of agents’ application, this paper aims to propose a conceptual model based on a multi-agent system (MAS) to control each step of the KDD process.

This paper reports the empirical findings of a survey conducted among academic and industrial sectors in Tehran, Iran. In this survey, the participants answered a questionnaire about the main factors of designing a suitable model for the KDD process based on MAS. The factor analysis reveals important insights of previous models developed by various researchers.

This research uses the survey results to find six critical success factors, continuity in refinement and improvement; learning and acting concurrently; loosely or tightly coupled approach for using technologies; cooperative, dynamic and flexible environment; documentation and reporting; and extracting and evaluating knowledge intelligently, for a proper conceptual model of the KDD process based on MAS.

The proposed model reflects all aspects of the KDD process by applying the intelligent agents for each process steps. In addition, this research only considers the Iran society; hence, it cannot be generalized to other nations, and it may need further research in other countries and to be implemented in real-world business domains.

This research helps organizations to adopt a proposed model and implement a KDD process to advantage the valuable knowledge that exists in their data resources.

The applications of multi-agent systems (MASs) are considered to be among the most promising paradigms for detailed investigations and reliable problem-solving methods, and MAS applications make it possible for researchers and practitioners to better understand complex systems. Although a number of prior studies have been conducted to address complex issues that arise from construction projects, few studies have summarised the applications and discussed the capacity of MASs from the perspective of construction management. To fill the gap, this paper provides a comprehensive literature review of MAS applications from the perspective of construction management.

Web of Science and Scopus are the most commonly used international databases in conducting the literature reviews. A total of 86 relevant papers published in SCI-Expanded, SSCI and Ei Compendex journals related to the application of MASs from the perspective of construction management are selected to be analysed and discussed in this paper.

Based on the 86 collected publications, the utilisations of MASs to support the management of the supply chain and the improvement of project performance are identified from the perspective of construction management, the characteristics and barriers of current MAS applications are analysed, a framework for developing agent-based models to address complex problems is proposed, and future research directions of MAS applications are discussed.

This review can serve as a useful reference for scholars to enhance their understanding of the current research and guide future research on MASs. The proposed framework can help build agent-based models to address complex problems in construction management.

Obviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach.

To the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

As seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor.

To the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored.

For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled.

Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility.

The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems.

The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis.

A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

The purpose of this paper is to determine how high-tech firms should choose between independent research and development and technology introduction as well as to ascertain the effects of the three elements of competitive dynamics on the evolution of innovative behavior-based decisions and competitive results.

This paper describes the construction of an evolutionary game model and a multi-agent-based model of innovative behavior-based decisions by heterogeneous high-tech firms. The models are used to analyze the evolution path and evolutionarily stable strategy of innovative behavior-based decisions. In addition, multi-agent-based simulation is used to gain insight into the effects of competitive dynamics on the dynamic evolution of innovative behavior-based decisions.

This paper reveals four evolutionary equilibrium states of the innovation behavior-based decisions of high-tech firms. Based on the findings, these overall evolutionary trends are not affected by the timing of competitive market entry or the intensity of competition. In addition, simulated evidence is added that the timing of competitive market entry is an important factor affecting market-leading innovative strategies and dynamic competition results, and competition intensity is closely related to the evolutionary speed of innovation behavior-based decisions.

The key contribution of this paper is its new view of innovative behavior-based decisions from a competitive dynamics perspective. The new competitive dynamics-based framework for innovative behavior-based decisions of high-tech firms proposed in the paper can resolve the problem of obtaining a sustainable competitive advantage for high-tech firms in a competitive dynamics context.

The purpose of this paper is to reveal the pattern of passengers' transferring on occasion of a large crowd being stranded at transportation hubs (such as a bus station, railway station, airport, etc.) in climate disasters, and then propose the proper policy recommendations for the government to evacuate stranded passengers.

A model is established based on Bayesian network and influence diagram to catch the features of a passenger's decision‐making process, and the transition probabilities of passengers are revised on the basis of the theory of herd behaviors in information to describe the influence of group behaviors on passenger individuals. Subsequently, a multi‐agent model is developed in Repast platform in Java language, and simulation and analysis are also made.

The results of simulation show that it is possible to apply the theory of herd behaviors and the multi‐agent method in analyzing the effectiveness of government policies on evacuating stranded passengers in climate disasters.

The research of this paper has important practical significance for the government to developing policies to evacuating stranded passengers in climate disasters, and is a useful exploration to open up new methodologies for emergency management.