Topic
Simulation software
About: Simulation software is a research topic. Over the lifetime, 7872 publications have been published within this topic receiving 73733 citations.
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Book•
15 Jun 1994
TL;DR: This book provides an introduction and comprehensive reference to modeling and simulation techniques using computers and emphasizes applications in economics and the environmental sciences and contains a disk with simulation software (SIMPAS) and 50 system models.
Abstract: From the Publisher:
This book provides an introduction and comprehensive reference to modeling and simulation techniques using computers. It emphasizes applications in economics and the environmental sciences and contains a disk with simulation software (SIMPAS) and 50 system models.
1,029 citations
TL;DR: In this paper, an improved and easy-to-use battery dynamic model is presented, and the charge and the discharge dynamics of the battery model are validated experimentally with four batteries types.
Abstract: This paper presents an improved and easy-to-use battery dynamic model. The charge and the discharge dynamics of the battery model are validated experimentally with four batteries types. An interesting feature of this model is the simplicity to extract the dynamic model parameters from batteries datasheets. Only three points on the manufacturer’s discharge curve in steady state are required to obtain the parameters. Finally, the battery model is included in the SimPowerSystems simulation software and used in a detailed simulation of an electric vehicle based on a hybrid fuel cell-battery power source. The results show that the model can accurately represent the dynamic behaviour of the battery.
1,020 citations
Book•
01 Jan 2015
TL;DR: This paper presents a meta-simulation framework that automates the very labor-intensive and therefore time-heavy and therefore expensive process of designing and implementing Discrete--Event Simulation Software.
Abstract: PRINCIPLES. Principles of Simulation (J. Banks). Principles of Simulation Modeling (A. Pritsker). METHODOLOGY. Input Data Analysis (S. Vincent). Random Number Generation (P. L'Ecuyer). Random Variate Generation (R. Cheng). Experimental Design for Sensitivity Analysis, Optimization, and Validation of Simulation Models (J. Kleijnen). Output Data Analysis (C. Alexopoulos & A. Seila). Comparing Systems via Simulation (D. Goldsman & B. Nelson). Simulation Optimization (S. Andradottir). Verification, Validation, and Testing (O. Balci). RECENT ADVANCES. Object--Oriented Simulation (J. Joines & S. Roberts). Parallel and Distributed Simulation (R. Fujimoto). On--Line Simulation: Need and Evolving Research Requirements (W. Davis). APPLICATION AREAS. Simulation of Manufacturing and Material Handling Systems (M. Rohrer). Simulation in the Automobile Industry (O. Ulgen & A. Gunal). Simulation of Logistics and Transportation Systems (M. Manivannan). Simulation of Healthcare (F. McGuire). Simulation of Service Systems (R. Laughery, et al.). Military Simulation (K. Kang & R. Roland). Discrete--Event Simulation of Computer and Communication Systems (A. Hartmann & H. Schwetman). Simulation and Scheduling (A. Kiran). PRACTICE OF SIMULATION. Guidelines for Success (K. Musselman). Managing the Simulation Project (V. Norman & J. Banks). How Discrete--Event Simulation Software Works (T. Schriber & D. Brunner). Software for Simulation (J. Banks). Index.
742 citations
TL;DR: There is a disconnect between research in simulation optimization--which has addressed the stochastic nature of discrete-event simulation by concentrating on theoretical results of convergence and specialized algorithms that are mathematically elegant--and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature.
Abstract: Probably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. The recent integration of optimization techniques into simulation practice, specically into commercial software, has become nearly ubiquitous, as most discrete-event simulation packages now include some form of ?optimization? routine. The main thesis of this article, how-ever,is that there is a disconnect between research in simulation optimization--which has addressed the stochastic nature of discrete-event simulation by concentratingon theoretical results of convergence and specialized algorithms that are mathematically elegant--and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature (e.g., genetic algorithms, tabu search, artificial neural networks). A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrasting these approaches with the algorithms implemented in commercial software. The article concludes with the author's speculations on promising research areas and possible future directions in practice.
652 citations
01 Jan 2002
TL;DR: There is a disconnect between research in simulation optimization—which has addressed the stochastic nature of discrete-event simulation by concentrating on theoretical results of convergence and specialized algorithms that are mathematically elegant—and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature.
Abstract: Probably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquitous, as most discrete-event simulation packages now include some form of “optimization” routine. The main thesis of this article, however, is that there is a disconnect between research in simulation optimization—which has addressed the stochastic nature of discrete-event simulation by concentrating on theoretical results of convergence and specialized algorithms that are mathematically elegant—and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature (e.g., genetic algorithms, tabu search, artificial neural networks). A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrasting these approaches with the algorithms implemented in commercial software. The article concludes with the author’s speculations on promising research areas and possible future directions in practice. (Simulation Optimization; Simulation Software; Stochastic Approximation; Metaheuristics)
637 citations