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Both system dynamics (SD) and agent-based modeling (ABM) have been used in simulation-based group dynamics research. To combine the advantages of both simulation approaches, the concept of SD-ABM hybrid simulation has been proposed. However, research efforts to compare the effectiveness of modeling approaches between the hybrid and non-hybrid models in the context of group dynamics study are rare. Against this background, this study aims to propose an agent-embedded SD (aeSD) modeling approach and demonstrate its advantages when compared to pure SD or ABM modeling approaches, based on a research case on construction workers’ social absenteeism.

The authors introduce an aeSD modeling approach to incorporate individual attributes and interactions among individuals in an SD model. An aeSD model is developed to replicate the behavior of an agent-based model previously developed by the authors to study construction workers’ group behavior regarding absenteeism. Then, the characteristics of the aeSD model in comparison with a pure ABM or SD model are demonstrated through various simulation experiments.

It is demonstrated that an aeSD model can capture the diversity of individuals and simulate emergent system behaviors arising from interactions among heterogeneous agents while holding the strengths of an SD model in identifying causal feedback loops and policy testing. Specifically, the effectiveness of the aeSD approach in policy testing is demonstrated through examples of simulation experiments designed to test various group-level and individual-level interventions to control social absence behavior of workers (e.g. changing work groupings, influencing workgroup networks and communication channels) under the consideration of the context of construction projects.

The proposed aeSD modeling method is a novel approach to how individual attributes of agents can be modeled into an SD model. Such an embedding-based approach is distinguished from the previous communication-based hybrid simulation approaches. The demonstration example presented in the paper shows that the aeSD modeling approach has advantages in studying group dynamic behavior, especially when the modeling of the interactions and networks between individuals is needed within an SD structure. The simulation experiments conducted in this study demonstrate the characteristics of the aeSD approach distinguishable from both ABM and SD. Based on the results, it is argued that the aeSD modeling approach would be useful in studying construction workers’ social behavior and investigating worker policies through computer simulation.

This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance.

The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables.

The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context.

This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain.

This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties.

This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance.

The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.

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.

Agent-based computer simulation gives new possibilities to model service supply chains which combine flow of people, geographical elements, demand patterns and service rates. The purpose of this paper is to demonstrate by using an example how agent-based modeling can be used for health service supply chain design.

Generic structure of agent-based service supply chain modeling is described. The presented example is healthcare supply chain with service distribution and service location problem. Main focus in presentation on model building, actual case data are not discussed.

In context of service supply chain, agent-based modeling has advantages compared to traditional discrete event approach. Agent-based simulation allows modeling of interactions of autonomous agents.

Reach of service for each geographical area may be used as a constraint for building service distribution network. Service supply chains consist of service providers and flow of customers with given geographical locations. Key performance indicators can be assessed in combination with service footprint.

Availability of geographical population data and agent-based simulation gives new possibility for service supply chain models.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

Uneven distribution and mistarget beneficiaries are among problems encountered during post-disaster relief operations in 2010 Mount Merapi eruption. The purpose of this paper is to develop an empirically founded agent-based simulation model addressing the evacuation dynamics and to explore coordination mechanism and other promising strategies during last-mile relief delivery.

An agent-based model which was specified and parameterized by empirical research (interviews and survey) was developed to understand the mechanism of individual decision making underlying the evacuation dynamics. A set of model testing was conducted to evaluate confidence level of the model in representing the evacuation dynamics during post-disaster of 2010 Mount Merapi eruption. Three scenarios of last-mile relief delivery at both strategic and operational levels were examined to evaluate quantitatively the effectiveness of the coordination mechanism and to explore other promising strategies.

Results indicate that the empirically founded agent-based modeling was able to reproduce the general pattern of observable Internal Displaced Persons based on government records, both at micro and macro levels, with a statistically non-significant difference. Low hazard perception and leader-following behavior which refuses to evacuate are the two factors responsible for late evacuation. Unsurprisingly, coordination through information sharing results in better performance than without coordination. To deal with both uneven distribution and long-term demand fulfillment, coordination among volunteers during aid distribution (at downstream operation) is not sufficient. The downstream coordination should also be accompanied with coordination between aid centers at the upstream operation. Furthermore, the coordination which is combined with other operational strategies, such as clustering strategy, using small-sized trucks and pre-positioning strategy, seems to be promising. It appears that the combined strategy of coordination and clustering strategy performs best among other combined strategies.

The significant role of early evacuation and self-evacuation behavior toward efficient evacuation indicates that human factor (i.e. hazard perception and cultural factor) should be considered in designing evacuation plan. Early warning system through both technology and community empowerment is necessary to support early evacuation. The early warning system should also be accompanied with at least 69 percent of the population performing self-evacuation behavior for the effective evacuation. As information sharing through coordination is necessary to avoid redundant efforts, uneven distribution and eventually to reduce unmet demand, the government can act as a coordinating actor to authorize the operation and mobilize the resources. The combination of coordination and another strategy reducing lead time such as clustering analysis, thus increasing responsiveness, is seemly strategy for efficient and effective last-mile relief distribution.

Literature on coordination is dominated by qualitative approach, which is difficult to evaluate its effectiveness quantitatively. Providing realistic setting of the evacuation dynamics in the course of the 2010 Mount Merapi eruption, the empirically founded agent-based model can be used to understand the factors influencing the evacuation dynamics and subsequently to quantitatively examine coordination mechanisms and other potential strategies toward efficient and effective last-mile relief distribution.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

Social media facilitates consumer exchanges on product opinions and provides comprehensive knowledge of online products. The interaction between consumers and e-retailers evolves into a collective set of dynamics within a complex system. Agent-based modeling is well suited to stimulate such complex systems. The purpose of this paper is to integrate agent-based model and technique for order performance by similarity to ideal solution (TOPSIS) to simulate decision behaviors of e-retailers in competitive online markets.

An agent-based network model using the TOPSIS driven by actual price data is developed. The authors ran an experimental model to simulate interactions between online consumers and e-retailers and to record simulation data. A nonparametric test is used to conduct data analysis and evaluate the sensibility of parameters.

Simulation results showed that different profits could be obtained for various brands under different social network structures. E-retailers could achieve more profits through cross-selling than single-selling; however, the highest profits can be achieved when some adopt cross-selling, whereas others use single-selling. From a game perspective, the equilibrium for price-adjustment frequency can be determined from the simulation data. Thus, price adjustment differences significantly affect e-retailer profit.

This study provides new insights into the evolutionary dynamics of online markets. This work also indicates how to build an integrated simulation model with an agent-based model and TOPSIS and how to use an integrated simulation model and interpret its results.

There are two purposes in this paper. The first one is to explore the impact mechanisms and paths of service-oriented manufacturing (SOM) on the sustainability performance of green manufacturing firms and to pay particular attention to the mediating role of enterprise costs in the relationship between the two. The second one is to reveal the dynamic process and laws of SOM influencing on the sustainability performance of green manufacturing enterprises.

This paper employs a combination of dynamic and static research from two aspects. On the one hand, based on 495 green manufacturing firms in China, the authors verify the SEM of SOM, enterprise cost and sustainability performance of green manufacturing firms through empirical research, analyze the path relationship and mediation effect, and demonstrate its complex impact mechanism. On the other hand, the authors use agent-based modeling and simulation to reveal the dynamic influence of SOM on the sustainability performance of green manufacturing enterprises and explore its dynamic laws and future development trends.

The study results indicate that (1) SOM has a direct positive effect on firm sustainability performance. (2) Reducing firm costs has a positive effect on firm sustainability performance, and firm costs play a partial mediating role in the mechanism by which SOM influences firm sustainability performance. (3) The dynamic impact of SOM on sustainability performance increases significantly over time. (4) Significant firm heterogeneity exists in the process by which SOM improves sustainability performance. (5) There are differences in the promotion of SOM for economic performance, environmental performance and social performance.

This paper makes two main contributions. First, compared with previous research on sustainability performance focusing on a certain dimension, this paper organically integrates the three dimensions of sustainability performance, regards them as a whole and considers the paths to improve sustainability performance. Second, this paper focuses on the complex relationship among SOM, enterprise costs and sustainability performance and reveals different sustainability performance improvement paths and dynamic impact processes, thereby expanding and deepening previous research on sustainability on the subject. These contributions lead to a better understanding of the driving forces and realization path for green manufacturing firms' sustainability performance from an integrated static and dynamic perspective.

The purpose of this paper is to present the research for improvement of the operating room in a hospital, to reduce patient waiting time and increase the resource utilization.

Value stream mapping (VSM) is used to represent the entire operating room (OR) process and patient flow to identify problems. Agent‐based simulation (ABS) is applied to model human behaviors in the OR operation. Agents perform human factors in the simulation model with autonomous and interactive functions actively.

The research outcomes prove the effectiveness of integrated VSM and ABS to improve decision making in human‐centred healthcare environments.

Because the state is dynamically changed, the task priority needs to be updated dynamically. The nurse schedule to transport patients between different units is to be detailed.

Long waiting lists in hospitals lead to patient dissatisfaction and care quality reduction. It is crucial to identify inefficiency and to improve the healthcare delivery effectively.

The paper shows how VSM and ABS are integrated in the modeling for the dynamic OR planning. It improves the simulation modeling of healthcare delivery.