Discrete-event Modeling And Simulation Pdf Creator

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Conceptual Modeling CM is a fundamental step in a simulation project.

Documentation Help Center. Entities are discrete items of interest in a discrete-event simulation. Entities can pass through a network of queues, servers, gates, and switches during a simulation.

A conceptual modeling framework for discrete event simulation using hierarchical control structures

Healthcare systems aspire to offer an effective and efficient provision of health service without compromising on quality. In particular, simulation methods have been widely used by healthcare researchers and practitioners.

This paper is written as a guide for building hospital simulation models and is based on the author's experience and the published literature. Two points of view emerge in this paper supporting each other: a conceptual view and a technical view.

Initially, conceptual issues are discussed, including topics such as framing and specification, level of model detail, and data requirements. Second, a technical point of view buttresses the discussion from the methodological side and three simulation methods are evaluated, namely: Discrete Event Simulation, System Dynamics, and Agent-Based Simulation.

Hospitals are one of the most important links in the healthcare service chain and their quality directly affects people's lives. One such method is computer simulation. For example, a simulation model of a hospital's radiology department could be used to better understand the impact that a new Magnetic Resonance Imaging scanner might have on the hospital's quality of service.

Most of the papers in the hospital simulation modelling area open with a sentence indicating the financial size of the healthcare sector and the need for improved efficiency. The intention of this paper is to act as a guide to support researchers or practitioners building hospital simulation models.

These are often related to the importance of engagement by hospital management Lane et al, A useful general discussion of modelling for example in the U. This paper is organised in two parts. The first part focuses on conceptual modelling and the issues related to model definition, conceptualisation and framing of hospital processes, and data requirements. In addition, some ideas about the level of detail and level of generality are presented.

The second part of the paper is devoted to simulation methodologies. A hypothetical hospital system is defined and modelled using each of these methods. Conceptual modelling is a blueprint of the model that is to be built and is ideally independent from simulation software. This definition suggests that once a conceptual model is built, a computer model can be developed using available simulation software.

On the other hand, Pidd argues that, in most of today's simulation software, there is capability of constructing a conceptual model on the go, while the modeller builds the actual simulation. Users of simulation software can at least build a flow diagram or a process diagram with a drag and drop from already defined simulation building blocks. As indicated in these definitions, a conceptual model is independent from simulation software; however, although not mentioned in the literature, a conceptual model is dependent on the simulation methodology.

This is because modellers tend to think in terms of the simulation methodology. It is therefore natural that the differences between the chosen simulation methodologies affect the conceptual model.

Regardless of the simulation methodology adopted, the process of conceptual modelling should come before the model building Pidd, A conceptual model helps the modeller to develop an understanding of the problem situation, to determine the modelling objectives and to identify the system's boundaries, inputs, outputs, and constructing elements and their interactions.

Once these specifications are on hand, a model is easier to build. However, these specifications may also change during the model building, because the modeller may realise that something is missing or misunderstood once they have started modelling. This suggests that conceptual model is an ongoing process throughout the entire simulation project.

Although it makes sense to build a conceptual model before building a simulation model, there is no consensus on how a model is built, what instruments are used for it, and how it is represented.

Robinson suggests four methods of representation in common use: component list, process flow diagram, logic flow diagram, and activity cycle diagram.

On the other hand, Onggo presents a methodology for unified conceptual model representation. When the focus is to model patient processes in a hospital, process flow diagrams are the best choice since they are easy to build, can be better understood, and a good way of communication between experts and non-experts.

Depending on the scale of the hospital simulation project the modeller is engaged in, the most difficult problem is to tackle complexity. In fact, this is true for any simulation project, and conventional rules Henriksen, can be applied to a hospital's context. More specifically, hospitals could be viewed as simple input—output systems whereby patients arrive from different sources, take different treatment routes, and are discharged. This extremely simple representation is, however, typically insufficient for understanding real-world hospital complexity.

One possible classification of hospital units could be a functional division of units. Modelling these units to form a hospital model is more manageable than considering the hospital as one whole unit. This still holds true if you are to build only one unit of a hospital given that there may still be functional divisions within that unit. For example, in an Emergency Department ED context, reception could be thought of as a sub-unit, medical test facilities as another etc.

Aggregation is another method that could be employed to reduce complexity. For example, operating rooms and anaesthetics services could be represented implicitly under specialist care services, since the use of operating room capacity is a function of specialist care that is constrained by the number of surgeon teams and number of beds.

A simple process flow of a hospital, with its component units, inputs, and outputs is illustrated in Figure 1. It must be emphasised that this is a very simple possible representation and is not sufficient for representing the full content of a simulation model, but it is an attempt to simplify the complexity involved.

Simple diagrams such as these are useful starting points. Hospitals are complex organisations, and therefore a simulation model that includes all services provided by a hospital is unlikely to be feasible to build.

For a simulation modeller, the main task is to reduce the complexity to a level at which modelling objectives can be achieved. Within a hospital, there is a range of services provided that is directly or indirectly related to patient care. The modeller's task is to choose which of these services and activities are to be modelled, based on the objectives of the simulation project. The frame drawn specifies the model boundaries. However, there are two important points regarding these services.

First, although some of these services work independently, most interact with each other, and therefore they are dependent on other services. For example, an ED may interact with radiology and laboratory departments, and outpatient clinics with operating rooms patients seen in clinic may subsequently need surgery. Second, some services are shared by specialist care provider units in hospital, and therefore the performance of one unit may affect another's performance.

Because of shared resources, shortfalls in one service may have knock-on effect to other services. One of the grand challenges of hospital modelling is the interactions between services and activities. For a modeller, it is important to link the services provided with physical activities and units in hospital since a patient will be represented by an entity and flows of entities, or in other words patient flows will be the main emphasis in the simulation model.

Some services have their own dedicated physical units, such as ED, pharmacy, and laboratory, but some take place elsewhere. For example, general surgery beds may be located in wards A and B, urology beds in ward D, and so on.

It is also worth noting that these services require different types of resources, including humans, beds, and equipment. It is recommended that from time to time the modeller remind themselves of what will be modelled. Of course, the answer to this question is that what is to be modelled is determined by the objectives of the study. This will be the most important point in the study since it will also determine the level of detail in the model. Include only what you need as this will save time and effort; more detail means more time to add to the model and more data to analyse.

Draw a frame and concern yourself only with what is inside of the frame. To build credible models, reasonable data are required. After conceptualisation of the hospital processes, the modeller must consider the inputs.

A model's input is also associated with the level of detail of the model. The more detailed the structure of the model is, the more inputs the model requires, and, if data are unavailable, some required details may be compromised. Today's information technology helps modellers with this since extensive amount of data are collected routinely in hospitals, though mostly for financial purposes.

However, there is a danger of having this much data; modellers may fall in love with the data Pidd, Thoughtful and careful data analysis is an important phase in the development of most simulation models, and is therefore very important when dealing with a complex system such as a hospital. Modelling a hospital requires information and data from various sources such as hospital's information system sometimes called Patient Administration System , interviews with staff, and personal observations at the hospital.

All of these sources help the modeller gain understanding of the important aspects that need to be simulated. Interviews and visits offer a qualitative view, whereas other data offer a quantitative view of the real system. Visits are useful if a general view of the hospital is required and can also fill some information gaps that cannot be explained only with numerical data, such as the outlook of rooms and wards.

Although huge amounts of data are available from today's information systems in health care hence it may seem like a heaven for simulation modellers , it may be difficult to use these data to estimate system parameters that characterise a hospital. The modeller may use commercial off-the-shelf software to analyse hospital data, or sometimes may consider using specific software developed for this purpose.

It seems plausible that there is a relationship between the modelling objectives, the level of detail, and the generality of a model.

It also seems plausible that beyond a certain point, the greater the level of detail, the greater the likelihood that a model will cease to be generic. Modelling objectives in a simulation project focus on activities that affect the performance of a hospital as measured in waiting times and this, in turn, has a major influence on the level of detail and, in turn, generality.

In Figure 2, these relationships are illustrated. Two examples to further explain the relationships in Figure 2 are now provided. Adding the layout detail increases the level of detail but decreases generality or model reuse since every ED may have a different physical layout. However, instead of taking physical dimensions into account, staff travel times can be included in staff service times. This decreases the level of detail, in terms of number of inputs required, and increases generality, in terms of less dependence on physical processes.

A second example arises from bed management. A number of ward rounds may occur in inpatient wards during the day and these may affect the length of stay of patients. Suppose that a modeller is simulating the length of stay for inpatient beds to evaluate bed occupancy in a number of wards. Considering the ward-round times in the model causes the level of detail to increase, which requires more inputs and more simulation code in the model.

This detail, however, decreases generality since the ward-round procedures are different across the hospitals. However, if the objective is to only simulate the length of stay, the model need not necessarily include the ward-round processes. Instead, it may be reflected implicitly by using stationary distributions disregarding special procedures of ward-round processes.

A change in ward-round frequency may require some revisions to the stationary distributions. There are a number of simulation methods that can be used for building hospital models. In this system, there are emergency patients who are seen in ED, and then sent to one of three ward groups in the hospital. Electively admitted patients use operating room resources and are then sent to a ward.

The 9 Best Simulation Software

Thanks to Luis Gustavo Nardin and Jakub Lelo for valuable feedback that helped to improve this tutorial. This article shows how to create, and run, a simulation model with the JavaScript-based simulation framework OESjs available on sim4edu. In OES, a model normally defines various types of objects and events, but OES also supports models without explicit events, using fixed-increment time progression corresponding to implicit time events "ticks" , which is a popular approach in social science simulation. Simulation is used widely today: in many scientific disciplines for investigating specific research questions, in engineering for testing the performance of designs, in education for providing interactive learning experiences, and in entertainment for making games. A dynamic system may be subject to discrete or continuous state changes.

Web-Based Simulation with OESjs

Simulation of homogeneous Pois. An alternative approach is the thinning technique that consists on rejecting some of the simulated events. When simulating a nonhomogenous Poisson process on a fixed time interval, we build a dmf out of the intensity function by scaling it. The geometric or exponential brownian motion is a continuous-time stochastic process that is used to model stock prices. The univariate marginals of a geometric brownian motion follow a log-normal distribution.

A guide for building hospital simulation models

Healthcare systems aspire to offer an effective and efficient provision of health service without compromising on quality. In particular, simulation methods have been widely used by healthcare researchers and practitioners. This paper is written as a guide for building hospital simulation models and is based on the author's experience and the published literature. Two points of view emerge in this paper supporting each other: a conceptual view and a technical view.

This site features information about discrete event system modeling and simulation. It includes discussions on descriptive simulation modeling, programming commands, techniques for sensitivity estimation, optimization and goal-seeking by simulation, and what-if analysis. Advancements in computing power, availability of PC-based modeling and simulation, and efficient computational methodology are allowing leading-edge of prescriptive simulation modeling such as optimization to pursue investigations in systems analysis, design, and control processes that were previously beyond reach of the modelers and decision makers. Enter a word or phrase in the dialogue box, e.

Improved Time Representation in Discrete-Event Simulation

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Improved Time Representation in Discrete-Event Simulation

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