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Simulation Techniques
Published in Harry G. Perros, An Introduction to IoT Analytics, 2021
There are two major categories of simulation depending upon the system that is being modeled: discrete-event and continuous event. Discrete-event simulation is used to model systems whose state takes discrete values. For instance, let us assume that we want to simulate the queueing of IoT requests in a server. The state of this system is discrete, because it is depicted by the number n of requests queueing for service, n = 0, 1, 2, …, which is a discrete variable. Discrete-event simulation is used to model systems such as IoT systems, computer networks, production systems, etc. Continuous-event simulation, on the other hand, is totally different to discrete-event simulation and it is based on differential equations. It is used for systems whose state is a continuous variable, such as the position in the trajectory of a rocket. In this chapter, we deal with discrete-event simulation techniques.
Digital Simulation
Published in Louis Scheffer, Luciano Lavagno, Grant Martin, EDA for IC System Design, Verification, and Testing, 2018
Logic simulation is a special case of the more general discrete event simulation methods, which were initially developed in the 1960s [5]. Discrete event simulation is a method of representing the behavior of a system over time, using a computer. The system may be either real or hypothetical, but its state is assumed to change over time due to some combination of external stimulus and internal state. The fundamental idea is that the behavior of any system can be decomposed into a set of discrete instants of time at which things happen. Those instants are called events, and the “things that happen” are state changes. This is very analogous to the way we digitize continuous physical phenomena, like audio sampling. In essence, we digitize a time-based process by dividing it up into discrete events. It is easy to see that with a fine enough granularity, one can get an adequately accurate representation for just about any purpose.
Digital Simulation
Published in Luciano Lavagno, Igor L. Markov, Grant Martin, Louis K. Scheffer, Electronic Design Automation for IC System Design, Verification, and Testing, 2017
Logic simulation is a special case of the more general discrete event simulation methods, which were initially developed in the 1960s [5]. Discrete event simulation is a method of representing the behavior of a system over time, using a computer. The system may be either real or hypothetical, but its state is assumed to change over time due to some combination of external stimulus and internal state. The fundamental idea is that the behavior of any system can be decomposed into a set of discrete instants of time at which things happen. Those instants are called events, and the “things that happen” are state changes. This is very analogous to the way we digitize continuous physical phenomena, like audio sampling. In essence, we digitize a time-based process by dividing it up into discrete events. It is easy to see that with a fine enough granularity, one can get an adequately accurate representation for just about any purpose.
Introducing Lean practices through simulation: A case study in an Italian SME
Published in Quality Management Journal, 2023
Stefano Frecassetti, Bassel Kassem, Kaustav Kundu, Matteo Ferrazzi, Alberto Portioli-Staudacher
The combined use of case studies and simulations has led to the study of these countless benefits. Through the collection of data from a real case, we were able, through the use of Simulation, to enrich and consolidate the analysis, thanks to the countless advantages given by this methodology. Firstly, discrete-event Simulation allows the dynamic analysis of production systems to identify possible improvements in the “AS-IS” state and the possibility of introducing improvements in future "TO-BE" scenarios. We have better understood the system in its initial phase through the Simulation. Moreover, the great advantage of the Simulation is being able to analyze possible solutions for future implementations without disrupting the environment of a company. Thanks to the Simulation, it is possible to validate possible solutions in a time-lapse and avoid investing money in solutions without having the certainty that they could carry concrete advantages. One of the fundamental advantages of Simulation is that it can tolerate much less restrictive modeling assumptions, unlike exact analytical methods that rely on more restrictive assumptions.
Offshore platform for containerized cargo redistribution: a new concept and simulation-based performance study
Published in Maritime Policy & Management, 2021
Xinhu Cao, Shuhong Wang, Chenhao Zhou, Ning Wu
The simulation model is developed by using ARENA, a commercial simulation software for discrete event simulation modelling. The simulation logic is illustrated in Figures 7–9, which can be used as a guide to build an Arena model. In this paper, ‘container ship arrival’ is modelled by a ‘Create’ module, ‘queue in somewhere’ is modelled by a ‘Hold’ module, ‘If condition is met’ utilizes the ‘Decide’ module, ‘filling process’ is modelled by a ‘Process’ module, and ‘Release container and terminate simulation’ is based on a ‘Dispose’ module. Note that as ARENA is following entity driven concept, which means that each entity will execute the process one by one. To control the container flow, multiple ‘Hold’ and ‘Decide’ are needed. As the logics are general, the model can be developed by using other commercial simulation software, such as AutoMod as well.
Automating computer simulation and statistical analysis in production planning and control research
Published in International Journal of Computers and Applications, 2018
Jeng Feng Chin, Joshua Prakash, Shahrul Kamaruddin, Melissa Chea Ling Tan
Discrete event simulation is one form of simulations where the state variable changes at discrete points in time in a system is modeled [21]. Discrete event simulation [20, 23–25] is (a) able to provide a risk-free environment for testing a new system; (b) able to handle complex systems involving uncertainty; (c) able to model characteristics and movement of product discretely; (d) easily repeated; and (e) less costly than real experiments. Formally, discrete event simulation is applied in the association step of the methodology. In this research, discrete event simulation is used because changes in output in response to a discrete change in input generate data which reveal considerable insight regarding the systems under investigation. The procedures in discrete event simulation are discussed next, and these include selection of parameters, variables and performance measures, model construction, verification, and validation.