Simulation
Welcome to this graduate course on Discrete-Event Simulation, a hybrid discipline that combines knowledge and techniques from Operations Research (OR) and Computer Science (CS) (Figure 1). Due to the fast and continuous improvements in computer hardware and software, Simulation has become an emergen...
| Autores: | , |
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| Formato: | livro |
| Fecha de publicación: | 2012 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/21036 |
| Acesso em linha: | https://hdl.handle.net/2117/21036 |
| Access Level: | acceso abierto |
| Palavra-chave: | Numerical analysis--Simulation methods Anàlisi numèrica 65C Probabilistic methods, simulation and stochastic differential equations Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Simulació |
| Resumo: | Welcome to this graduate course on Discrete-Event Simulation, a hybrid discipline that combines knowledge and techniques from Operations Research (OR) and Computer Science (CS) (Figure 1). Due to the fast and continuous improvements in computer hardware and software, Simulation has become an emergent research area with practical industrial and services applications. Today, most real-world systems are too complex to be modeled and studied by using analytical methods. Instead, numerical methods such as simulation must be employed in order to study the performance of those systems, to gain insight into their internal behavior and to consider alternative (“what-if”) scenarios. Applications of Simulations are widely spread among different knowledge areas, including the performance analysis of computer and telecommunication systems or the optimization of manufacturing and logistics processes. This course introduces concepts and methods for designing, performing and analyzing experiments conducted using a Simulation approach. Among other concepts, this course discusses the proper collection and modeling of input data and system randomness, the generation of random variables to emulate the behavior of the real system, the verification and validation of models, and the analysis of the experimental outputs. Figure |
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