Discrete and Continuous Simulation Theory and Practice
Auteurs : Bandyopadhyay Susmita, Bhattacharya Ranjan
When it comes to discovering glitches inherent in complex systems?be it a railway or banking, chemical production, medical, manufacturing, or inventory control system?developing a simulation of a system can identify problems with less time, effort, and disruption than it would take to employ the original. Advantageous to both academic and industrial practitioners, Discrete and Continuous Simulation: Theory and Practice offers a detailed view of simulation that is useful in several fields of study.
This text concentrates on the simulation of complex systems, covering the basics in detail and exploring the diverse aspects, including continuous event simulation and optimization with simulation. It explores the connections between discrete and continuous simulation, and applies a specific focus to simulation in the supply chain and manufacturing field. It discusses the Monte Carlo simulation, which is the basic and traditional form of simulation. It addresses future trends and technologies for simulation, with particular emphasis given to .NET technologies and cloud computing, and proposes various simulation optimization algorithms from existing literature.
- Includes chapters on input modeling and hybrid simulation
- Introduces general probability theory
- Contains a chapter on Microsoft® Excel? and MATLAB®/Simulink®
- Discusses various probability distributions required for simulation
- Describes essential random number generators
Discrete and Continuous Simulation: Theory and Practice defines the simulation of complex systems. This text benefits academic researchers in industrial/manufacturing/systems engineering, computer sciences, operations research, and researchers in transportation, operations management, healthcare systems, and human?machine systems.
Introduction to Simulation. Monte Carlo Simulation. Introduction to Probability Theory. Probability Distributions. Introduction to Random Number Generators. Random Variate Generation. Steady-State Behavior of Stochastic Processes. Statistical Analysis of Steady-State Parameters. Computer Simulation. Manufacturing Simulation. Manufacturing and Supply Chain Simulation Packages. Supply Chain Simulation. Simulation in Various Disciplines. Simulation of Complex Systems. Simulation with Cellular Automata. Agent-Based Simulation. Continuous System Simulation. Introduction to Simulation Optimization. Algorithms for Simulation Optimization. Simulation with System Dynamics. Simulation Software. Future Trends of Simulation. References. Index.
Date de parution : 06-2014
15.6x23.4 cm
Date de parution : 04-2017
15.6x23.4 cm
Thèmes de Discrete and Continuous Simulation :
Mots-clés :
Cellular Automata; Random Number; software; Simulation Optimization; event; Agentbased Modeling; supply; Simulation Software; chain; Random Number Generator; genetic; Beer Distribution Game; algorithm; SIMULATION SOFTWARE APPLICATIONS; study; Ordinary Differential Equations; cellular; True Random Number Generators; automata; Manufacturing Simulation; language; Supply Chain; Discrete Event Simulation; Linear Congruential Generator; Mathematical Expression; MONTE CARLO; Cumulative Distribution Function; Object Oriented Programming Languages; Bullwhip Effect; Random Variables; Parallel Machine Scheduling Problem; Continuous System Simulation; CRUDE MONTE CARLO; Net Framework; Supply Chain Simulation