A Brief Introduction to Discrete-Event Simulation Modeling and Analysis Ming zhou, PhD, Associate Professor Indiana State University, Terre Haute, IN. 47809, USA (812)237-3983; imming@isugw. instate. edu
A Brief Introduction to Discrete-Event Simulation Modeling and Analysis Ming Zhou, PhD., Associate Professor Indiana State University, Terre Haute, IN 47809, USA (812)237-3983; imming@isugw.indstate.edu
Slide 1: Introduction to Simulation s Systems and models of a system o Concept of a system(input, output, process resources, behavior, performance measures o Interest of studying a system(design, planning control, improvement, and optimization) Models of a system: representation of real systems ● Physical models Logical or mathematical models
Slide 1: Introduction to Simulation Systems and models of a system ⚫ Concept of a system (input, output, process, resources, behavior, performance measures) ⚫ Interest of studying a system (design, planning, control, improvement, and optimization) ⚫ Models of a system: representation of real systems ⚫ Physical models ⚫ Logical or mathematical models
System and models of system System Study/experiment Study/experiment with the with a model of actual system the system Physical Mathematical or model logical model Analytical Simulation mode model
System and models of system System Study/experiment with the actual system Study/experiment with a model of the system Physical model Mathematical or logical model Simulation model Analytical model
Slide 2: Introduction to Simulation s Studying a system via analytical model V.s. simulation model(prescriptive V.S. descriptive models) Analytical model >Performance measures are expressed as mathematical functions of input parameters, result is exact and close form solution applicable only to simple problems ● Simulation model→> a logical model that is evaluated(numerically) over a time period of interest. Performance measures are estimated from model-generated data with statistical procedures applicable to systems of any complexity
Studying a system via analytical model v.s. simulation model (prescriptive v.s. descriptive models) ⚫ Analytical model → Performance measures are expressed as mathematical functions of input parameters, result is exact and close form solution, applicable only to simple problems. ⚫ Simulation model → a logical model that is evaluated (numerically) over a time period of interest, Performance measures are estimated from model-generated data with statistical procedures, applicable to systems of any complexity. Slide 2: Introduction to Simulation
Slide 3: Introduction to Simulation g Why use simulation models? It is often of interest to study a real-world system to generate knowledge on its behavior or dynamics. However it is usually necessary to use a simulation model for the following reasons Experimentation with the real system is often disruptive (e.g. study of a flow-line manufacturing process) Experimentation with the real system is not cost-effective (e.g. study of large logistic/distribution center Experimentation with the real system is simply impossible (e.g. study of space rocket launching operations
Slide 3: Introduction to Simulation Why use simulation models? It is often of interest to study a real-world system to generate knowledge on its behavior or dynamics. However it is usually necessary to use a simulation model for the following reasons: Experimentation with the real system is often disruptive (e.g. study of a flow-line manufacturing process) Experimentation with the real system is not cost-effective (e.g. study of large logistic/distribution center) Experimentation with the real system is simply impossible (e.g. study of space rocket launching operations)