Agent-based modelling and mental simulation for resilience engineering in air transport

Agent-based modeling and simulation (ABMS) a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science, in addition to traditional deductive and inductive reasoning (Axelrod 1997b). Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model of shopper behavior using spreadsheets.

Agent-based modeling and simulation in architecture

Background and Purpose: Agent-based modelling and simulation (ABS) is growing in many areas like, e.g., management, social and computer sciences. However, the similar trend does not seem to occur within the field of business process management (BPM), even though simulation approaches like discrete event simulation or system dynamics are well established and widely used. Thus, in our paper we investigate the advantages and disadvantages of agent-based modelling and simulation in the field of BPM in simulation experiments. Design/Methodology/Approach: In our research, we investigate if there is a necessity for ABS in the field of BPM with our own simulation experiments to compare traditional and ABS models. For this purpose, we use simulation framework MAREA, which is a simulation environment with integrated ERP system. Our model is a complex system of a trading company selling computer cables. For the verification of our model, we use automated process discovery techniques. Results: In our simulations, we investigated the impact of changes in resources’ behavior on the outcome of company’s order to cash process (O2C). Simulations experiments demonstrated that even small changes might have statistically significant effect on outcomes of the processes and decisions based on such outcomes. Simulation experiments also demonstrated that the impact of randomly distributed fluctuations of well-being have a diminishing tendency with the increasing number of sales representatives involved in the process. Conclusions: Our research revealed several advantages and disadvantages of using ABS in business process modelling. However, as we show, many of them were at least partially addressed in the recent years. Thus, we believe that ABS will get more attention in the field of BPM similarly to other fields like, e.g., social sciences. We suggested areas in BPM simulations, e.g., modelling of resources, be it human or technological resources, where there is a need for ABS.

Abstract. One of the most interesting aspects of modelling and simulation study is to describe the real world phenomena that have specific properties; especially those that are in large scales and have dynamic and complex behaviours. Studying these phenomena in the laboratory is costly and in most cases it is impossible. Therefore, Miniaturization of world phenomena in the framework of a model in order to simulate the real phenomena is a reasonable and scientific approach to understand the world. Agent-based modelling and simulation (ABMS) is a new modelling method comprising of multiple interacting agent. They have been used in the different areas; for instance, geographic information system (GIS), biology, economics, social science and computer science. The emergence of ABM toolkits in GIS software libraries (e.g. ESRI’s ArcGIS, OpenMap, GeoTools, etc) for geospatial modelling is an indication of the growing interest of users to use of special capabilities of ABMS. Since ABMS is inherently similar to human cognition, therefore it could be built easily and applicable to wide range applications than a traditional simulation. But a key challenge about ABMS is difficulty in their validation and verification. Because of frequent emergence patterns, strong dynamics in the system and the complex nature of ABMS, it is hard to validate and verify ABMS by conventional validation methods. Therefore, attempt to find appropriate validation techniques for ABM seems to be necessary. In this paper, after reviewing on Principles and Concepts of ABM for and its applications, the validation techniques and challenges of ABM validation are discussed.

Agent-based modeling and simulation (ABMS) may fit well with entrepreneurship research and practice because the core concepts and basic premises of entrepreneurship coincide with the characteristics of ABMS. However, it is difficult to find cases where ABMS is applied to entrepreneurship research. To apply ABMS to entrepreneurship and organization studies, designing a conceptual model is important; thus to effectively design a conceptual model, various mixed method approaches are being attempted. As a new mixed method approach to ABMS, this study proposes a bibliometric approach to designing agent-based models, which establishes and analyzes a domain corpus. This study presents an example on the venture creation process using the bibliometric approach. This example shows us that the results of the multi-agent simulations on the venturing process based on the bibliometric approach are close to each nation's surveyed data on the venturing activities. In conclusion, by the bibliometric approach proposed in this study, all the agents and the agents' behaviors related to a phenomenon can be extracted effectively, and a conceptual model for ABMS can be designed with the agents and their behaviors. This study contributes to the entrepreneurship and organization studies by promoting the application of ABMS.

Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters required to be calibrated grows. This leads to the ABMS equivalent of the “curse of dimensionality”. We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration. We show that surrogate assisted methods perform better than the standard sampling methods. In addition, we show that the XGBoost and Decision Tree SMs are most optimal overall with regards to our analysis.

Reproducible modeling and simulation research has been identified as one of the Modeling and Simulation (M&S) Grand Challenge activities. Recently, uncertainty quantification has seen a renewed emphasis. While methods for verification and validation (V&V) have been widely developed for discrete-event simulations, newer simulation approaches such as the agent-based, agent-directed, and multi-agent simulation approaches introduce new V&V challenges. The active elements in these newer approaches have greater heterogeneity, e.g., every agent can be unique, with complex attributes and behaviors. Those behaviors can result in actions based on interaction with other agents, the environment, and even the outcome of simulated or artificial intelligence. The simulation spaces are often less constrained, e.g., rather than a network of servers and queues, the space can be continuous 2D Euclidian space with multiple associated geographic information systems (GIS) layers influencing the behavior of the actors. Over the last decade, a multitude of techniques has been used in agent-based modeling and simulation (ABMS) to perform V&V as well as replication and reproducibility (R&R) of the models. In this chapter, we present an overview of contributions in V&V, quality assurance (QA), and R&R of simulation studies, with special focus on ABMS. We also discuss the lessons learnt in V&V and replication from a series of simulation experiments using agent-based models (ABMs).

Agent-based modeling and simulation (ABMS) has been applied to various domain problems. ABM consists of multiple agents and environments and focuses on their interactions. While such characteristics lead to its current popularity, they partially makes hard for the model calibration. Model calibration is generally performed by tuning model parameters, but in the ABM case, its interaction structure should be considered as well due to its huge influences to the results. To resolve this problem, this paper suggests a self-evolving ABMS framework. During the self-evolving process, ABM structure as well as parameters are explored and exploited for the model calibration. In particular, we adopt reconfigurable modeling in the proposed simulation framework, which is derived from Discrete Event System Specification (DEVS) formalism. This paper introduces a housing ABM, and the case study using this model shows that the accuracy of the model prediction was significantly increased through the self-evolving process.

Land conversion is one complex problem which includes actors and factors with different social levels. In the process of land use change, every small change in decision-making methods used by individuals may significantly affect the outcomes. Agent-based modeling and simulation (ABMS) is a common approach to analyze and simulate the process of land use change as the result of individual decisions. This paper firstly describes the general problem of agricultural land conversion (ALC) in Jambi Province, Indonesia, in particular in connection to the smallholders’ role. It is then followed by the brief explanation on factors influencing the smallholders’ behaviors. Then, conceptual framework of ABMS in analyzing land conversion as one kind of the land use change/cover (LUCC) processes is presented enriched with agent topologies and the decision-making processes on the agricultural land conversion problem. Finally the proposed model is illustrated through a preliminary case study in Jambi Province, and some scenario on the effect of interaction between farmers and government is described. The framework is still a general approach to analyze simple LUCC problem using ABMS approach.

Healthcare workers (HCWs) are often exposed to nosocomial infection when caring for patients with Ebola Virus Disease (EVD). During the 2014-2016 EVD outbreak in West Africa, more than 200 HCWs died of EVD in Sierra Leone. To determine the factors that are important for preventing infection among HCWs during EVD outbreak, we used agent-based modeling and simulation (ABMS) by focusing on education, training and performance of HCWs. Here, we assumed 1, 000 HCWs as "agents" to analyze their behavior within a given condition and selected four parameters (P1-P4) that are important in the prevention of infection: "initially educated HCWs (P1)", "initially educated trained (P2)", "probability of seeking training (P3)" and "probability of appropriate care procedure (P4)." After varying each parameter from 0% to 100%, P3 and P4 showed a greater effect on reducing the number of HCWs infected during EVD outbreak, compared with the other two parameters. The numbers of infected HCWs were decreased from 897 to 26 and from 1,000 to 59, respectively, when P3 or P4 was increased from 0% to 100%. When P2 was increased from 0% to 100%, the number of HCWs infected was decreased from 166 to 44. Paradoxically, the number of HCWs infected was increased from 56 to 109, when P1 was increased, indicating that initial education alone cannot prevent nosocomial infection. Our results indicate that effective training and appropriate care procedure play an important role in preventing infection. The present model is useful to manage nosocomial infection among HCWs during EVD outbreak.

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.

Coal discrepancy refers to the variance between actual coal-mined production and planned. This study aims to explore the contributing factors to the coal discrepancy phenomenon and its magnitude of impact, especially in the upstream area of the coal mining value chain. The author carried out a focus group discussion (FGD) involving experts at one of the largest coal mining companies in Indonesia. The result will then be verified by empirical data related to discrepancies that was collected from January to December 2023. The output variables then become the input of the simulation using Agent-Based Modelling and Simulation (ABMS) to explore the emergent properties. The study discovered that the top three significant contributing factors to this phenomenon are the change in mining sequence (47.1%), the accuracy of the geological model (36.5%), and operational loss (5.5%). The simulation result shows that coordination among the pit technical or planner, geologist, and operation crew is crucial for fostering value co-creation within the company, which in turn optimises this phenomenon. The statistical analysis reveals that the collaboration has a significant correlation of − 0.844 to coal achievement time and 0.912 to coal mined achievement per unit of time, with a significance level value of 0.000 (< 0.01).

Many cities around the world have experienced an increase in population and pose challenges in planning and managing the freight transportation within the city. Off-peak hour delivery (OPHD) is a strategy of delivering goods in the afternoon and evening to reduce congestion during peak periods, increase the efficiency of delivery companies, and reduce emissions. This research proposed a model of city logistics in the retail supply chain by implementing OPHD in the logistics system. The aim of this study is to explore the extent of potential implementation of OPHD in urban logistics. An agent-based modeling and simulation (ABMS) approach is applied to model the autonomous and dynamic interactions of stakeholders involved in city logistics systems. Preliminary results are presented in this paper to provide illustrations of the model. It shows that OPHD can significantly reduce the transportation cost and emissions cost, and ABMS is able to provide results that are relatively close to pilot test drive research which is carried out in real world.

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.

This paper discusses devising a methodology for developing a heuristic approach for planning authorities and policy makers to investigate the integration of electric mobility charging infrastructure in metropolitan cities, particularly in the UK. This study is a part of a PhD research project; it presents a part of its overall methodology, which will be employed in a smaller-scale pilot project to obtain initial findings. An agent-based modelling and simulation (ABMS) technique is proposed to represent the phenomenon and analyze it. This study covers the development of the model, emphasizing how it would reflect Electric Vehicles (EVs) current users' behaviours and their charging habits. The aim of the PhD research is to examine whether the current electric-mobility charging infrastructure can support current users and whether it can accommodate future potential users or if the planning authorities and policy makers need to forecast and plan for more charging points. This in return shall potentially provide a better accessibility to e-mobility and support its market, partly by better informed location decision making.