1,223 research outputs found
Data underlying the paper: An agent-based process mining architecture for emergent behavior analysis
No full textThe dataset contains a collection of experiment results and event logs generated. The experiment comprises a job-shop scheduling problem, implemented in a discrete-event simulation model. The raw experiment results are given from which event log files can be generated by following the steps as described in this data paper or the referred academic paper. A collection of event log files is given, as well as the raw files. The logs include the filtered part of the case study as presented in the paper "An agent-based process mining architecture for emergent behavior analysis" by Rob Bemthuis, Martijn Koot, Martijn Mes, Faiza Bukhsh, Maria-Eugenia Iacob, and Nirvana Meratnia
Emergent Behaviors in a Resilient Logistics Supply Chain
Get PDFThis PhD dissertation addresses vulnerabilities in logistics supply chains, such as disruptions from pandemics, natural disasters, and geopolitical tensions. It underscores the complexity of supply chains, likening them to socio-technical systems where resilience is key for managing unexpected events and thriving amidst adversity. The focus is on leveraging smart business objects—exemplified by “smart pallets” with sensing and computational capabilities—to augment real-time decision-making and resilience in supply chains. When strategically positioned within the supply network, these smart pallets can provide key insights into the movement of goods, enabling a rapid response to disruptions through real-time monitoring and predictive analytics. The dissertation investigates centralized, decentralized, and hybrid approaches to decision-making within these networks. Centralized methods ensure uniformity but may neglect local specifics, while decentralized ones offer adaptability at the risk of inconsistency. A hybrid model seeks to balance these extremes, combining broad guidelines with local autonomy for optimal resilience. This research aims to explore how such smart objects can anticipate and react to emergent behaviors, thereby augmenting supply chain resilience beyond mere performance indicators to actively managing and adapting to disruptions. Through various chapters, the dissertation offers an exploration, from designing resilient architectures and evaluating business rules in real-time to mining these rules from data and adapting them to evolving circumstances. Overall, this work presents a nuanced view of resilience in supply chains, emphasizing the adaptability of business rules, the importance of technological evolution alongside organizational practices, and the potential of integrating novel techniques such as process mining with multi-agent systems for better decision-making and operational efficiency
Process mining and agent-based simulation: A harmonious blend!
Get PDFIn this work, we explore the potential synergy between process mining and agent-based modeling and simulation (ABMS). ABMS is a powerful tool for analyzing complex socio-technical systems and has advanced data-driven capabilities. Process mining is an emerging discipline that combines data mining and process modeling to gain insights into process execution by analyzing event data. This includes process discovery, conformance checking, and process enhancement. Our research examines the role of process mining in the ABMS paradigm. We classify existing research, present related use cases, and suggest future research directions for utilizing process mining with ABMS. Our findings offer initial guidance for researchers and practitioners and highlight promising avenues for further investigation in this exciting area of research
Lost in Models? Structuring Managerial Decision Support in Process Mining with Multi-criteria Decision Making
Get PDFProcess mining is increasingly adopted in modern organizations, producing numerous process models that, while valuable, can lead to model overload and decision-making complexity. This paper explores a multi-criteria decision-making (MCDM) approach to evaluate and prioritize process models by incorporating both quantitative metrics (e.g., fitness, precision) and qualitative factors (e.g., cultural fit). An illustrative logistics example demonstrates how MCDM, specifically the Analytic Hierarchy Process (AHP), facilitates trade-off analysis and promotes alignment with managerial objectives. Initial insights suggest that the MCDM approach enhances context-sensitive decision-making, as selected models address both operational metrics and broader managerial needs. While this study is an early-stage exploration, it provides an initial foundation for deeper exploration of MCDM-driven strategies to enhance the role of process mining in complex organizational settings
Discovering Agent Models using Process Mining: Initial Approach and a Case Study
Get PDFAgent-based modeling is widely used for modeling and simulation of self-organizing sociotechnical systems that are composed of distributed autonomous agents. In these systems, macro level behaviors emerge from local micro level behaviors of agents that follow rules and interact with each other and the environment. Although the individual agents' behaviors are typically described by sets of simple rules, the many interactions, heterogeneous populations, and complex topologies can make it challenging, or even impossible, to predict or steer the emergent behaviors beyond micro levels. Hence, the actual behaviors of such systems are generally hard to know beforehand, and they need to be observed to extract realistic models. In this paper, we propose a proof-of-concept approach to discover agents' underlying models from log data generated from their behaviors, utilizing process mining. To conceptualize and demonstrate our initial approach, we use an illustrative example of the popular Schelling's model of segregation. Our findings provide encouraging initial evidence on how agent models can be extracted utilizing process mining techniques
Using Process Mining for Face Validity Assessment in Agent-Based Simulation Models: An Exploratory Case Study
Get PDFIn the field of simulation, the key objective of a system designer is to develop a model that performs a specific task and accurately represents real-world systems or processes. A valid simulation model allows for a better understanding of the system’s behavior and improved decision-making in the real world. Face validity is a subjective measure that assesses the extent to which a simulation model and its outcomes appear reasonable to an expert based on a superficial examination of the simulator’s realism. Process mining techniques, which are novel data-driven methods for obtaining real-life insights into processes based on event logs, show promise when combined with effective visualization techniques. These techniques can augment the face validity assessment of simulation models in reflecting real-life behavior and play a key role in supporting humans conducting such assessments. In this paper, we present an approach that utilizes process mining techniques to assess the face validity of agent-based simulation models. To illustrate our approach, we use the Schelling model of segregation. We demonstrate how graphical representation, immersive assessment, and sensitivity analysis can be used to assess face validity based on event logs produced by the simulation model. Our study shows that process mining in combination with visualization can strongly support humans in assessing face validity of agent-based simulation models
IoT-Enabled Multi-Agent Simulation for Hazard Detection and Safety in Construction
Get PDFConstruction sites are inherently hazardous environments, often prone to accidents that compromise worker safety and disrupt operational efficiency. Despite advancements in safety protocols, the dynamic nature of these sites necessitates innovative solutions to proactively mitigate risks. The goal of this study is to investigate the integration of Internet of Things (IoT)-enabled knowledge sharing within a multi-agent simulation framework to enhance hazard detection and worker safety. The proposed system models construction sites as dynamic environments where agents represent workers, tasks, and hazards. By leveraging IoT technology, agents can share hazard information in real-time, reducing the need for direct hazard encounters and potentially optimizing task efficiency. Implemented using the Tropos methodology and NetLogo, the simulation compares scenarios with and without IoT support, analyzing task completion rates, hazard interactions, and navigation patterns. Results show that IoT-enabled knowledge sharing improves hazard avoidance, boosts task efficiency, and supports safer navigation. Expert feedback validates the model’s alignment with real-world practices and suggests refinements such as predefined pathways and dynamic hazard representation. This study highlights how integrating IoT within multi-agent systems can enhance construction site safety and productivity
Towards automation for rework reduction in software development:An approach
Get PDFRework in software development significantly impacts project costs and efficiency, often consuming a substantial portion of a project team’s time. Rework may arise due to various factors such as poor planning or incomplete requirements, with the cost of resolving these issues escalating the longer they remain undetected. Information technologies (ITs), known for their strict logic-based operations, offer promising solutions for automating rework detection and prevention at various stages of the software development cycle. The objective of this paper is to provide an overview that assists software development organizations in identifying and mitigating rework in their operations. We present a four-step, tiered approach, developed through a literature review, for mapping rework root causes, early indicators of rework, operational mitigation strategies, and potential automation solutions. The approach is assessed via a survey of IT experts and an application to a Dutch IT company. The results indicate that the proposed approach could potentially reduce rework by approximately 14.8% and aid organizations in identifying and prioritizing an effective automation solution. Consequently, this approach may help businesses shorten the development cycle and promote efficient resource utilization
The SF-36: a simple, effective measure of mobility disability for epidemiological studies
No full textBackgroundMobility disability is a major problem in older people. Numerous scales exist for the measurement of disability but often these do not permit comparisons between study groups. The physical functioning (PF) domain of the established and widely used Short Form-36 (SF-36) questionnaire asks about limitations on ten mobility activities.ObjectivesTo describe prevalence of mobility disability in an elderly population, investigate the validity of the SF-36 PF score as a measure of mobility disability, and to establish age and sex specific norms for the PF score.MethodsWe explored relationships between the SF-36 PF score and objectively measured physical performance variables among 349 men and 280 women, 59-72 years of age, who participated in the Hertfordshire Cohort Study (HCS). Normative data were derived from the Health Survey for England (HSE) 1996.Results32% of men and 46% of women had at least some limitation in PF scale items. Poor SF-36 PF scores (lowest fifth of the gender-specific distribution) were related to: lower grip strength; longer timed-up-and-go, 3m walk, and chair rises test times in men and women; and lower quadriceps peak torque in women but not men. HSE normative data showed that median PF scores declined with increasing age in men and women.ConclusionOur results are consistent with the SF-36 PF score being a valid measure of mobility disability in epidemiological studies. This approach might be a first step towards enabling simple comparisons of prevalence of mobility disability between different studies of older people. The SF-36 PF score could usefully complement existing detailed schemes for classification of disability and it now requires validation against them
Complex calcium ferrites in the blast furnace process: Fluxed sinter formation and SFCA reduction under simulated conditions
No full textCivil Engineering and Geoscience
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