A simulation study requires the definition of a conceptual model; a representation of a problem within a system that is derived from theory or observations [11,14,15]. This conceptual representation should integrate different components, such as objectives, inputs, outputs, content, boundaries, assumptions, and simplifications [16,17]. Later, the conceptual model is transferred into computer software that helps healthcare professionals to comprehend the relationship between the input and output variables of the real-world system [1,18].
Discrete-event simulation (DES), also referred to as a time-to-event model, is ideal for complex problems, such as healthcare ones [9,19]. DES is a computer-based operation research technique that models different systems as networks of queues and activities [18] in order to assess, predict, and optimize a proposed or existing system, where changes occur at discrete epochs over time [8,20,21,22]. DES emerged from the manufacturing world, wherein Tocher developed the first language in the late 1950s for constructing a model to simulate a steel plant in the UK [7,23]. DES is often used to represent systems at an operational level, where transactions, processes, and the flow of individual entities, as well as the variability, are important factors [4,24]. Hence, DES models use events and typical quantities to imitate the observed behavior of the system by generating deterministic quantities or stochastic distributions [3]. DES can capture a systems behavior and interconnection effects, which result from the combinations of many random processes, coupled with the system structure [25]. Conversely, developing a DES model can be time consumingly (and costly), and it is heavily dependent on good quality data to inform the system behavior [24]. Users should, thus, balance the benefits and challenges of using the simulation approach.
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Building a DES model requires large amounts of quantitative numerical data [18]. It also needs a set of logical statements that are expressed in a computable form to describe how the entities change their state [27]. DES has been used in healthcare as a preferable modeling technique, given its flexibility in responding to scale changes, the level of detail, individual patient focus, stochastic factors affecting the system, the ease in changing the models components, waiting for the time-related performance, the existence of queues, and the visual representation of patient flows [17]. Although big data analysis is emerging as a technique for data modeling and simulation, it presents more challenges in processes subject to changing conditions and unexpected events [28].
Table 1 summarizes the characteristics of discrete-event simulation. While DES outputs can be point estimates, as well as ranges of values, the experimental results can be measured in terms of performance metrics, such as resource utilization, waiting times, the number of entities in queues, and the throughput of services or products, among others [29].
Abstract:Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.Keywords: discrete-event; simulation; modeling; healthcare; hospital; review; literature
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"@context": " ", "@type": "FAQPage", "mainEntity": [ "@type": "Question", "name": "What is Network Network Simulation?", "acceptedAnswer": "@type": "Answer", "text": "In computer network research, network simulation is a technique whereby a software program models the behavior of a network by calculating the interaction between the different network entities." , "@type": "Question", "name": "Is there download link available?", "acceptedAnswer": "@type": "Answer", "text": "Yes, the download link is available. To check the list of Top 10 Most Popular Network Simulation Tools. To download the network simulation tools. Know more" ]
Monte Carlo simulation (MCS) is a commonly used method for modeling complex systems having recursive processes and events that are impractical and time-consuming to test in the physical world. It generates numerous output scenarios by repeatedly picking random samples from an uncertain variable based on probability distribution. For instance, this mathematical tool has been used in large-cohort studies to assess whether regionalized intensive care could improve the outcome of patients who require mechanical ventilation [24]. Simulation of time-dependent probabilities of bacterial spread offered new means for testing various intervention strategies (antibiotics and infection control) in critical care practices [25]. MCS has been used with optimization techniques to enhance clinical decision making by maximizing antibiotics dosing [26, 27]. Markov simulation is another random-process modeling tool that often is used for economic evaluations when comparing different outcomes of complex medical interventions [28]. It has been used to investigate ICU clinical decision making by revealing evidence for sex-based risk difference in ICU patients [29]. Critical care applications of these tools have been recently reviewed by Kreke et al. [30].
DES models a system or process as an ordered sequence of individual events over time, that is, from the time of one event to the time of the next event. Hence, in a DES simulation, time is usually much shorter than real time.
Objects represent elements of the real system. They have properties, they relate to events, they consume resources, and they enter and leave queues over time. In the airplane takeoff and landing scenario mentioned earlier, the objects would be airplanes. In a health care system, objects might be patients or organs. In a warehouse system, the objects would be the products in stock. Objects are supposed to interact with each other or with the system and they can be created at any time during a simulation.
Time (as it happens in real life) is essential in simulation. To measure time, a clock is started at the beginning of a simulation and can then be used to track particular periods of time (departure or arrival time, transportation time, time spent with certain symptoms, and so on). Such tracking is fundamental because it allows you to know when the next event should occur.
The EndRelation method is basically a translation of the probability table for determining the chance of a relationship ending. It uses a uniform random variable, which produces a random value in the range [0, 1] equivalent to producing an acceptance percentage. The distributions dictionary is created in the simulation class (described shortly) and holds pairs (event, probability distribution), thus associating every event with its distribution:
In this article I developed a discrete event simulation to see how a population evolves in time. The object-oriented approach proved very useful for obtaining readable, concise code that readers can try and improve if necessary.
ns-3 is a discrete-event network simulator for Internet systems, targeted primarily for research and educational use. ns-3 is free, open-source software, licensed under the GNU GPLv2 license, and maintained by a worldwide community.
Monte Carlo techniques to radiation transport play a significant role in modeling complex astrophysical phenomena. In this paper, we design an application model (IMCSim) of an Implicit Monte Carlo (IMC) particle code using the Performance Prediction Toolkit (PPT), a discrete-event simulation-based modeling framework for predicting code performance on a large range of parallel platforms. We present validation results for IMCSim. We then use the fast parameter scanning that such a high-level loop-structure model of a complex code enables to predict optimal IMC parameter settings for interconnect latency hiding. We find that variations in interconnect bandwidth have a significant effect on optimal parameter values. Our results suggest potential value using IMCSim as a pre-step to substantial IMC runs to quickly identify optimal parameter values for the specific hardware platform on which IMC runs. 2ff7e9595c
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