A modelling and simulation framework for health care systems.

International audienceIn this paper, we propose a new modeling methodology named MedPRO for addressing organization problems of health care systems. It is based on a metamodel with three different views: process view (care pathways of patients), resource view (activities of relevant resources), and organization view (dependence and organization of resources). The resulting metamodel can be instantiated for a specific health care system and be converted into an executable model for simulation by means of a special class of Petri nets (PNs), called Health Care Petri Nets (HCPNs). HCPN models also serve as a basis for short-term planning and scheduling of health care activities. As a result, the MedPRO methodology leads to a fast-prototyping tool for easy and rigorous modeling and simulation of health care systems. A case study is presented to show the benefits of the MedPRO methodology

Simulation Modelling Practice and Theory

The influx of data in the world today needs analysis that no one method can handle. Some reports estimated the influx of data would reach 163 zitabytes by 2025, hence the need for simulation and modeling theory and practice. Simulation and modeling tools and techniques are most important in this day and age. While simulation carries the needed work, tools for visualizing the results help in the decision-making process. Simulation ranges from a simple queue to molecular dynamics, including seismic reliability analysis, structural integrity assessment, games, reliability engineering, and system safety. This book will introduce practitioners, researchers, and novice users to simulation and modeling, and to the world of imagination

Discrete event modelling for evaluation and optimisation of power utility energy demand

Purpose: The cost and environmental impact of energy is driving better quantification of energy utilization in a business context. Determining an entire business electrical energy usage, inclusive of core operations and support activities, in a singular evaluation protocol is a challenge. The challenge is exasperated when changes occur in the business, where every change implies significant rework of the business energy calculations. This study develops a holistic energy determination model for the entire business requiring minimum inputs for energy re-calculation, when aspects of the business changes. Design/methodology/approach: The research adopts a quantitative approach enabled through a Discrete Event Model. The model is developed based on the activities performed in every functional area of the business. The activities are captured using business process science. The processes are then developed into a DES Model. The model development cycle includes data collection, model development and configuration, model validation and scenario models for optimization. Findings: A coal fired power generation business, with multiple sites is comprehensively simulated to evaluate the baseline electrical energy demand and associated CO2 emissions. The results are captured at various levels of the business including; Enterprise; site, business function and equipment level. The generation sites operational functions are identified as major electrical energy consumers. The adoption of Industry 4.0 technologies of Internet of Things, Big Data Analytics, mobility and automation demonstrate energy savings of 1% of total site demand. As the Industry 4.0 technologies are applied to a limited number of processes, the results demonstrate the capability of these technologies having a significant impact on electrical energy demand and CO2 emission when applied to a broader spectrum of business processes. Research limitations/implications: The research is limited to a multi-site energy generating company, which is a coal to energy business. Practical implications: The research has significant practical implications, mostly on the mechanisms to evaluate business energy utilisation. The ability to include all areas of the business is a key practical differentiator, as compared to traditional models focusing on operations only. Originality/value: The model is unique in that it is a model that is system agnostic to any production configuration, most especially changes in configuration. This implies that the model can be easily and quickly adapted with changes in the business. This implies the model proposed would be significantly more adaptable when compared to traditional approachesPeer Reviewe

Process Mining Contributions to Discrete-event Simulation Modelling

Background: Over the last 20 years, process mining has become a vibrant research area due to the advances in data management technologies and techniques and the advent of new process mining tools. Recently, the links between process mining and simulation modelling have become an area of interest. Objectives: The objective of the paper was to demonstrate and assess the role of process mining results as an input for discrete-event simulation modelling, using two different datasets, one of which is considered data-poor while the other one data-rich. Methods/Approach: Statistical calculations and process maps were prepared and presented based on the event log data from two case studies (smart mobility and higher education) using a process mining tool. Then, the implications of the results across the building blocks (entities, activities, control-flows, and resources) of simulation modelling are discussed. Results: Apart from providing a rationale and the framework for simulation that is more efficient modelling based on process mining results, the paper provides contributions in the two case studies by deliberating and identifying potential research topics that could be tackled and supported by the new combined approach. Conclusions: Event logs and process mining provide valuable information and techniques that could be a useful input for simulation modelling, especially in the first steps of building discrete-event models, but also for validation purposes

Actor based behavioural simulation as an aid for organisational decision making

Decision-making is a critical activity for most of the modern organizations to stay competitive in rapidly changing business environment. Effective organisational decision-making requires deep understanding of various organisational aspects such as its goals, structure, business-as-usual operational processes, environment where it operates, and inherent characteristics of the change drivers that may impact the organisation. The size of a modern organisation, its socio-technical characteristics, inherent uncertainty, volatile operating environment, and prohibitively high cost of the incorrect decisions make decision-making a challenging endeavor. While the enterprise modelling and simulation technologies have evolved into a mature discipline for understanding a range of engineering, defense and control systems, their application in organisational decision-making is considerably low. Current organisational decision-making approaches that are prevalent in practice are largely qualitative. Moreover, they mostly rely on human experts who are often aided with the primitive technologies such as spreadsheets and visual diagrams. This thesis argues that the existing modelling and simulation technologies are neither suitable to represent organisation and decision artifacts in a comprehensive and machine-interpretable form nor do they comprehensively address the analysis needs. An approach that advances the modelling abstraction and analysis machinery for organisational decision-making is proposed. In particular, this thesis proposes a domain specific language to represent relevant aspects of an organisation for decision-making, establishes the relevance of a bottom-up simulation technique as a means for analysis, and introduces a method to utilise the proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner