深圳大学管理学院：《运筹学》课程教学资源（PPT课件讲稿）专题（计算机仿真）计算机仿真 A Brief Introduction to Discrete-Event Simulation Modeling and Analysis

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)

Slide 4: Introduction to Simulation g Definition of simulation The process of designing and creating a computerized model of a real or proposed system for the purpose of numerical experiment to develop better understanding of the behavior/dynamics of that system under a given set of conditions g Simulation is a powerful tool for design, modeling, analysis, and optimization of systems. It is one of the target technologies for the 21st century identified by the NRC, NIST, NSF, IE, SME, ASME and many others

Definition of simulation The process of designing and creating a computerized model of a real or proposed system for the purpose of numerical experiment to develop better understanding of the behavior/dynamics of that system under a given set of conditions. Simulation is a powerful tool for design, modeling, analysis, and optimization of systems. It is one of the target technologies for the 21st century identified by the NRC, NIST, NSF, IIE, SME, ASME and many others … Slide 4: Introduction to Simulation

Slide 5: Introduction to Simulation ● Types of simulation o Static V.s. dynamic(Is time a factor?) o Continuous V.S. discrete(nature of change along time) Deterministic V.s. stochastic (Is randomness important c Application of simulation(See demos of application) Manufacturing Logistics transportation system Healthcare Service systems Military systems -Telecommunication Entertainment Robotics simulation

Types of simulation ⚫ Static v.s. dynamic (Is time a factor?) ⚫ Continuous v.s. discrete (nature of change along time) ⚫ Deterministic v.s. stochastic (Is randomness important?) Application of simulation (See demos of application) - Manufacturing - Logistics & transportation system - Healthcare - Service systems - Military systems - Telecommunication - Entertainment - Robotics simulation Slide 5: Introduction to Simulation

Slide 6: Introduction to Simulation s Application of simulation(in terms of decision making) System design and evaluation Process/system improvement and optimization Policy or strategy evaluation (What-if analysis) g Limitations of simulation Simulation cannot Provide exact solutions Find optimal solutions(in exact form) Compensate for inadequate data or poor management decisions Provide fast and easy solutions to complex problems

Application of simulation (in terms of decision making) - System design and evaluation - Process/system improvement and optimization - Policy or strategy evaluation (“What-if” analysis) Limitations of simulation: Simulation cannot: - Provide exact solutions - Find optimal solutions (in exact form) - Compensate for inadequate data or poor management decisions - Provide fast and easy solutions to complex problems Slide 6: Introduction to Simulation

Slide 7: Introduction to Simulation Implementation of simulation By hand (for small problems, e.g. Buffon Needle problem) By computers with software(3 levels of abstraction) Programming in general-purpose language(e.g C/C++, Pascal, Fortran) Simulation language(SIMAN, GPSS, SLAM High level simulators(GUI based, menu-driven, such as ARENAC, AutoModo, ProModelo) c Issues of modeling efficiency, flexibility and ease of implementation, hierarchical structure

Implementation of simulation ⚫ By hand (for small problems, e.g. Buffon Needle problem) ⚫ By computers with software (3 levels of abstraction): ⚫ Programming in general-purpose language (e.g., C/C++,Pascal, Fortran) ⚫ Simulation language (SIMAN, GPSS, SLAM) ⚫ High level simulators (GUI based, menu-driven, such as ARENA©, AutoMod©, ProModel©) Issues of modeling efficiency, flexibility and ease of implementation, hierarchical structure. Slide 7: Introduction to Simulation

Issues related to level of modeling constructs: modeling efficiency versus modeling flexibility Modeling flexibility Modeling efficiency l of modeling abstraction hierarchy

Issues related to level of modeling constructs: modeling efficiency versus modeling flexibility Level of modeling abstraction hierarchy Modeling efficiency Modeling flexibility