Summary: Simulation Modeling And Analysis  9781259254383  Averill M Law
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1 Basic Simulation Modeling

1.1 The Nature of Simulation
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For what kinds of problems has simulation been found a useful and powerful tool?
1. Designing and analyzing manufacturing systems.
2. Analyzing supply chains.
3. DeDesigning and operating transportation systems such as airports, freeways, ports and subways. 
Objections against simulation:
1. Models used to study largescale systems tend to be very complex, and writing computer programs to execute them can be an arduous task indeed.
2. A large amount of computer time is sometimes required.
3. There appears to be an unfortunate impressions that simulation is just an exercise in computer programming, albeit a complicated one. 
What's a system in the simulation
the facility process of interest 
1.2 Systems, Models and Simulations
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State of a system:
collection of variables necessary to describe a system at a particular time, relative to the objectives of a study. 
Ways to study a system:
Look at figure. 
1.3 DiscreteEvent Simulation
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What are two principal approaches for advancing the simulation clock?
1. Nextevent time advance
2. Fixedincrement time advance 
If the main program invokes event routine i, what are the activities that occur?
1. The system state is updated to account for the fact that an event of type i has occurred.
2. Information about system performance is gathered by updating the statistical counters.
3. The times of occurrence of future events are generated, and this information is added to the event list. 
1.3.1 TimeAdvance Mevhanism
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Why is it useful to write performance measures in integrals?
Computationally, as the simulation progresses, the integrals can easily be accumulated by adding up areas of rectangles. 
1.4.1 Problem Statement
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d(n): Expected Average delay in queue
An average over a discrete number of observations > discrete time statistic 
Expected utilization proportion of the server
serverbusy function divided by T(n)
= the expeteced proprotion of time during the simulation that the server is busy
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Topics related to Summary: Simulation Modeling And Analysis

Basic Simulation Modeling

Modeling Complex Systems  List Processing in Simulation

Modeling Complex Systems  A simple simulation language: simlib

Modeling Complex Systems  SingleServer queueing simulation with simlib

Simulation Software  Classification of Simulation Software

Simulation Software  Desirable software features

Simulation Software  ObjectOriented Simulation

Building valid, credible, and appropriately detailed simulation models  Verification of simulation computer programs

Building valid, credible, and appropriately detailed simulation models  Techniques for increasing model validity and credibility

Building valid, credible, and appropriately detailed simulation models  Statistical procedures for comparing realworld observations and simulation output data

Selecting input probability distributions

RandomNumber Generators  Linear Congruential Generators

RandomNumber Generators  Other kinds of Generators

Generating random variate  Introduction

Generating random variate  General approaches to generating random variate

Output Data Analysis for a Single System  Introduction

Output Data Analysis for a Single System  Transient and steadystate behaviour of a stochastic process

Output Data Analysis for a Single System  Types of simulations with regard to output analysis

Output Data Analysis for a Single System  Statistical Analysis for terminating simulations

Output Data Analysis for a Single System  Statistical Analysis for steadystate parameters

Comparing Alternative system Configurations  CI's for the difference between the expected responses of two systems

Comparing Alternative system Configurations  CI's for comparing more than two systems

Comparing Alternative system Configurations  Ranking and Selection

Variancereduction techniques  Control Variates

Variancereduction techniques  Conditioning

Experimental design and optimization  Introduction

Experimental design and optimization  2^k factorial designs

Experimental design and optimization  2^kp fractional factorial designs

Experimental design and optimization  Response surfaces and metamodels