Simulation & Modeling: Introduction

Some Definitions

  • Modeling: Construct a conceptual framework that describes a system
  • Simulation: Imitation of the real-world processes or systems
  • System: groups of objects that are joined together in some regular interaction toward the accomplishment of some purpose
  • System Environments: Outside the system. Changes here affect the system
  • System Components
    • Entity: Object of interest in the system
    • Attribute: Propery of an entity
    • Activity: A time period of a specified length
    • State: Collection of variables necessary to describe the system.
    • Event: Instantaneous occurrence that might change the state o f the system:
  • Model: a representation o f a system for the purpose of studying the system.
  • Discrete System: one in which the state variable(s) change only at a discrete set of points in time.
  • Continous System: one in which the state variable(s) change continuously over time.
  • Determinstic Model: Simulation models that contain no random variables are classified as deterministic.
  • Stochastic Model: A stochastic simulation model has one variables a s inputs.
  • Static Model: sometimes called a Monte Carlo simulation, represents a system at a particular point in time.
  • Dynamic Model:represents systems as they change over time

Steps in a Simulation Study

Figure 1: Simulation Study Steps

The steps of a simulation study are summarized in Figure fig-simulation-steps .

  • Problem Formulation: A statement of the problem that is clearly understood by both the simulation analyst and the client.

  • Setting of Objectives and Overall Plan: Project Proposal

  • Model Conceptualization and Building: You have to select the correct level of details and abstract the essential features.

  • Data Collection: Collect data for input analysis and validation

  • Model Translation: translate the system into a computer model

  • Verified?: the process of determining if the operational logic is correct.

  • Validated?: the process of determining if the model accurately represents the system.

  • Experimental Design: Deciding on elements related to the experiment (e.g. length of each run; number of runs)

  • Production Runs and Analysis: Statistical tests for significance

  • Documentation and Reporting