INTRODUCTION TO SIMULATION

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INTRODUCTION TO SIMULATION

• Week 1 Notes
• GE-703 Kumpaty

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Simulation

• Helps minimize the risk of making costly and
  sometimes fatal mistakes in real life
• Provides a virtual environment that helps prepare
  for real life situations, resulting in savings in
  time, money and even lives
• Flight simulator- reduces the risk of making
  costly errors in flight
• Systems simulation- reduces the risk of having
  systems that operate inefficiently (increased
  application in manufacturing and service system
  design and improvement)

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Simulation

• A way to reproduce the conditions of a situation,
  as by means of a model, for study or testing or
  training
• Simulation is the imitation of a dynamic system
  using a computer model (in our case) in order to
  evaluate and improve system performance
• Schriber Simulation is the modeling of a process
  or a system in such a way that the model mimics
  the response of the actual system to events that
  take place over time
• Discrete event simulation- models the effect of
  the events in a system as they occur over time.
  This employs statistical methods for generating
  random behavior and estimating model performance.
  (also called Monte Carlo methods)

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Simulation deals with models of systems

• System facility or process, actual or planned
• Examples abound
• Manufacturing facility ? Bank operation
• Airport operations (passengers, security, planes,
  crews, baggage)
• Transportation/logistics/distribution operation
• Hospital facilities (emergency room, operating
  room, admissions)
• Computer network ? Freeway system
• Business process (insurance office)
• Criminal justice system ? Chemical plant
• Fast-food restaurant ? Supermarket
• Theme park ? Emergency-response system

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Simulation deals with models of systems

• Model set of assumptions/approximations about
  how the system works
• Study the model instead of the real system
  usually much easier, faster, cheaper, safer
• Can try wide-ranging ideas with the model
• Make your mistakes on the computer where they
  dont count, rather than for real where they do
  count
• Often, just building the model is instructive
  regardless of results
• Model validity (any kind of model not just
  simulation)
• Care in building to mimic reality faithfully
• Level of detail
• Get same conclusions from the model as you would
  from system

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Types of Models

• Physical (iconic) models
• Tabletop material-handling models
• Mock-ups of fast-food restaurants
• Flight simulators
• Logical (mathematical) models
• Approximations and assumptions about a systems
  operation
• Often represented via computer program in
  appropriate software
• Exercise the program to try things, get results,
  learn about model behavior

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Studying Logical Models

• If model is simple enough, use traditional
  mathematical analysis get exact results, lots
  of insight into model
• Queueing theory
• Differential equations
• Linear programming
• But complex systems can seldom be validly
  represented by a simple analytic model
• Danger of over-simplifying assumptions model
  validity?
• Often, a complex system requires a complex model,
  and analytical methods dont apply what to do?

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Computer Simulation

• Broadly interpreted, computer simulation refers
  to methods for studying a wide variety of models
  of systems
• Numerically evaluate on a computer
• Use software to imitate the systems operations
  and characteristics, often over time
• Can be used to study simple models but should not
  use it if an analytical solution is available
• Real power of simulation is in studying complex
  models
• Simulation can tolerate complex models since we
  dont even aspire to an analytical solution

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Why Simulate?

• Trial-error approaches are expensive,
  time-consuming and disruptive (changes occur
  faster than the lessons can be learned!)
• The key to sound management decisions lies in the
  ability to accurately predict the outcomes of
  alternative course of action. Simulation
  provides precisely that kind of foresight.
• Simulating alternative production schedules,
  operating policies, staffing levels, job
  priorities, decision rules and the like allows
  the manager to accurately predict outcomes.

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Characteristics of SimulationStudy the system
measure, improve, design, control

• Captures system interdependencies
• Accounts for variability in the system
• Shows behavior over time
• Less costly, less disruptive, less time-consuming
• Virtually appealing
• Provides results that are easy to understand and
  communicate
• Provides info on multiple performance measures
• Forces attention to detail in a design (The devil
  is in the details!)
• Takes emotion out of the decision-making by
  providing objective evidence that is difficult to
  refute

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Different Kinds of Simulation

• Static vs. Dynamic
• Does time have a role in the model?
• Continuous-change vs. Discrete-change
• Can the state change continuously or only at
  discrete points in time?
• Deterministic vs. Stochastic
• Is everything for sure or is there uncertainty?
• Most operational models
• Dynamic, Discrete-change, Stochastic
• Ch. 2 discusses a static model and Ch. 11
  discusses continuous combined
  discrete-continuous models

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More on Simulation

• Evaluation phase (Simulation is an evaluation
  tool, not solution tool) First the model
  developed, run, get performance statistics..
• Doing Simulation the process of designing a
  model of a real system and conducting experiments
  with the model
• Steps Formulate a hypothesis, develop a
  simulation model, run simulation experiment, test
  the hypothesis, Yes-End No-go back to hypothesis
  formulation.
• Use of simulation- more now with the advent of
  technology/ computers
• Primary application- manufacturing warehousing
  and distribution systems
• Also in communication and visualization-
  simulation can stimulate interest tremendously in
  the model

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When Simulation Is Appropriate

• 1. Decisions should be of an operational nature.
  (quantitative and logical, not qualitative such
  as how to motivate a worker)
• 2. Processes should be well-defined and
  repetitive. (adherence to defined rules, even
  random behavior can be described using
  probability expressions and distributions)
• 3. Activities and events should be interdependent
  and variable. (If the activities dont interfere,
  then why simulation?)
• 4. The cost impact of the decision should be
  greater than the cost of doing the simulation.
• 5. The cost to experiment on the actual system
  should be greater than the cost of the
  simulation. (If it is economical to experiment
  on the actual system with minimal impact on the
  current operation, go ahead!)

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Cost effectiveness

• Cost initial investment on simulation software,
  often recovered after the first one or two
  projects.
• System costs with and without simulation (design,
  implementation, operation phases)
• Examples where simulation helped uncover and
  eliminate wasteful practices
• 1. GE Nuclear Energy ran a series of models,
  solving production problems cycle time for
  production of each part reduced by 50 and the
  output of a specialized reactor increased by 80.
• 2. A diagnostic radiology department was modeled
  to evaluate patient and staff scheduling the
  simulation model enabled improvements in
  operating procedures, thereby avoided major
  expansion in department size.

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Simulation by HandThe Buffon Needle Problem

• Estimate p (George Louis Leclerc, c. 1733)
• Toss needle of length l onto table with stripes d
  (gtl) apart
• P (needle crosses a line)
• Repeat tally proportion of times a line is
  crossed
• Estimate p by

Just for fun http//www.mste.uiuc.edu/reese/buffo
n/bufjava.html http//www.angelfire.com/wa/hurben/
buff.html
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Why Toss Needles?

• Buffon needle problem seems silly now, but it has
  important simulation features
• Experiment to estimate something hard to compute
  exactly (in 1733)
• Randomness, so estimate will not be exact
  estimate the error in the estimate
• Replication (the more the better) to reduce error
• Sequential sampling to control error keep
  tossing until probable error in estimate is
  small enough
• Variance reduction