Discrete event simulation (DES)
Discrete Event Simulation (DES) is a computational modeling technique used to represent complex systems by breaking them down into individual, sequential events. This methodology allows for the analysis of system behaviors by coding these events in a way that can be replicated on a computer. DES is particularly useful in studying how changes within a system can impact its overall functionality, enabling the identification of potential problems and facilitating training scenarios.
Simulations can range from entirely virtual models to those that incorporate real-life elements, such as driving simulators that replicate driving experiences without physical movement. Common applications include testing manufacturing processes, analyzing customer flow in retail environments, and training medical professionals using simulated patients.
Creating a DES involves several steps, including defining the situation to be modeled, collecting relevant data, and documenting the simulation for future reference. While DES is often more cost-effective and safer than real-life testing, its effectiveness relies heavily on the accuracy of the input data and assumptions made during the setup process. Limitations include potential costs for complex scenarios and the risk of inherent software errors that could affect results. Overall, DES serves as a powerful tool for experimentation and prediction across various fields.
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Discrete event simulation (DES)
Discrete event simulation (DES) is a way of taking the functions of a multifaceted system, breaking those functions down into individual components that occur in a specific order, and then coding them in such a way that they can be replicated. This process allows computers to be programmed to repeat the behavior so that it can be analyzed. DES provides a way that systems can be altered and adjusted to simulate variations in the system's functions so that the effects of these changes can be studied, problems in systems can be identified, and people can be trained or tested on the system and its functions.
Background
A simulation is a representation of a system that can be adjusted, adapted, and changed within certain parameters that reflect the limits of the actual system. The simulation may exist completely in a computer or may have real-life components; for example, a driving simulator may not physically move but will otherwise provide the "driver" with all the sensations and a simulation of the experiences of actually driving. This creates an interactive model of the original system that can be used in a number of ways.
Simulations can be used to test the function of a system before it is completed. A manufacturer planning a new engine might have a simulation programmed and tested before investing in the actual fabrication of the parts. They can also help to simulate specific scenarios experienced in a business or facility. For instance, DES can be programmed to determine how many people will reach the checkout counter at a grocery store at the same time on a busy Saturday or how a nuclear power plant will respond if there is a sudden increase in the demand for power. Simulations can also be used to imitate situations for training. For example, facilities can be made to train and test medical professionals where the equipment functions as real equipment would, and the "patients" are simulated and can be programmed to have different health problems.
Overview
The general steps to creating a discrete event simulation include the following:
identifying the situation to be simulated
determining the extent of the simulation
collecting data about the originating system
designing the model
determining that the model sufficiently replicates the original system
documenting the simulation for future comparison
designing an appropriate experiment
establishing the rules and conditions for the experiment
running the experiment
gathering and interpreting the results
making a recommendation for the next course of action
Individual models may use some or all of the steps. For example, the designer might decide to find a way to improve the flow of people out of a concert venue (identify the situation). The next steps would be to decide how many people would be leaving (determine the extent), study the site to see how many exits are available (collect data), design a program that includes these parameters (design the model), run a test that replicates the way people now leave (determine the model is sufficient), make notes about the design (document), design the experiment to try different ways of having people leave, run the experiment, determine whether more people were able to leave, and make a recommendation based on the results.
These models have a number of advantages over real-life testing. Computer-only simulations can be more cost-effective than real-life testing or training. For example, to test the checkout time for a grocery store on a busy Saturday morning in real life, a store might have cashiers stay after hours and bring in people to act as customers, having both the cashiers and the "customers" go through the motions of checking out. This process incurs costs of time and money. Replicating the scenario with a computer program can be done in a short period using only the computer analyst's time and effort, potentially saving a great deal of both time and money.
Another advantage is safety. Simulations can be devised to test such things as the strength of bridges under certain loads, the response of a power grid to an overload, or the strength of a new airplane design, all without putting any people at risk. One very common use of DES is in the medical field. Some schools that train medical professionals have entire simulated hospital wards where the equipment and the "patients" can be controlled by the trainers. Future physicians, nurses, and other professionals can learn on patients that are actually human replicas with computer-controlled functions that can be programmed for a wide range of health problems. Students can learn and be tested in these DES scenarios with no risk to living patients.
DES can also be used to model situations that cannot be experienced in the real world at a particular time. For instance, using historical data and information about current situations, discrete event simulators can be devised that can predict the future behavior of the stock market, the cost of precious metals in ten years, the rate of heart disease or diabetes over the next quarter-century, or environmental conditions in fifty years.
There are some limitations to the effectiveness of a discrete event simulation. Simulations can be costly, depending on the scenarios to be duplicated. Since their accuracy is dependent on the information that is provided in setting up and running the experiment, there is the potential for error. It is also possible for the software used for the simulation to have an inherent error or a "bug" that prevents accurate results. Finally, any discrete event simulation will only be as good as the assumptions used to make it. If, at any step of the process, the developer allows untested assumptions to become part of the process (for instance, assuming that all people will walk to the checkout area at the same speed during a grocery store simulation), the discrete event simulation will be flawed.
Bibliography
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"Discrete Event Simulation." York Health Economics Consortium, 2016, www.yhec.co.uk/glossary/discrete-event-simulation. Accessed 22 Dec. 2024.
“A Gentle Introduction to Discrete-Event Simulation.” Software Solutions Studio, 12 Mar. 2022, softwaresim.com/blog/a-gentle-introduction-to-discrete-event-simulation. Accessed 22 Dec. 2024.
Karnon, Jonathan, et al. "Modeling Using Discrete Event Simulation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4." Medical Decision Making, Sept./Oct. 2012, journals.sagepub.com/doi/pdf/10.1177/0272989X12455462. Accessed 222 Dec. 2024.
Wainer, Gabriel A., and Pieter J. Mosterman. Discrete-Event Modeling and Simulation: Theory and Applications.CRC, 2010.
"What Is Discrete-Event Simulation Modeling?" AnyLogic, www.anylogic.com/discrete-event-simulation. Accessed 22 Dec. 2024.