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Modeling and Analysis of Manufacturing Systems. Session 3 Simulation Models January 2001. 1. Definition of Simulation. Simulation is the imitation of the operation of a real world system over time.

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modeling and analysis of manufacturing systems

Modeling and Analysis of Manufacturing Systems

Session 3

Simulation Models

January 2001


definition of simulation
Definition of Simulation
  • Simulation is the imitation of the operation of a real world system over time.
  • Simulation involves the generation of an artificial history of the system and the drawing of inferences from it.


a first simulation example
A First Simulation Example
  • One teller bank
  • Customers arrive between 1 and 10 minutes apart with uniform probability.
  • Teller service times are between 1 and 6 minutes with uniform probability.
  • Goal: Simulate the bank’s operation until 20 customers are served.


  • Input data?
  • Model vs Reality?
  • Length of run?
  • Amount of runs?
  • Output analysis?


modeling concepts
Modeling Concepts
  • System: The real thing!
  • Model: A representation of the system.
  • Event: An occurrence which changes the state of the system.
  • Discrete vs Continuous Event Models.
  • Dynamic vs. Static Models.


modeling concepts contd
Modeling Concepts - contd
  • System state variables: All information required to characterize the system.
  • Entity: An object in the simulation.
  • Attributes: Entity characteristics.
  • Resources: A servicing entity.
  • Lists and list processing: Queues.
  • Activities and delays.


modeling structures
Modeling Structures
  • Process-Interaction Method
  • Event-Scheduling Method
  • Activity Scanning
  • Three-Phase Method


advantages of simulation
Advantages of Simulation
  • Decision aid.
  • Time stretching/contraction capability.
  • Cause-effect relations
  • Exploration of possibilities.
  • Diagnosing of problems.
  • Identification of constraints.
  • Visualization of plans.


advantages of simulation contd
Advantages of Simulation -contd.
  • Building consensus.
  • Preparing for change.
  • Cost effective investment.
  • Training aid capability.
  • Specification of requirements.


disadvantages of simulation
Disadvantages of Simulation
  • Training required.
  • Interpretation of results required.
  • Time consuming/expensive.
  • Inappropriately used.


application areas
Application Areas
  • Manufacturing/ Materials Handling
  • Public and Health Systems
  • Military
  • Natural Resource Management
  • Transportation
  • Computer Systems Performance
  • Communications


steps in simulation modeling
Steps in Simulation Modeling
  • Problem Formulation
  • Goal Setting
  • Model Conceptualization
  • Data Collection
  • Model Translation
  • Verification and Validation
  • Experimental Design


steps in simulation contd
Steps in Simulation -contd.
  • Production Runs and Analysis
  • Documentation/Reporting
  • Implementation


input data representation
Input Data Representation
  • Random Numbers and Random Variates

X = (1/) ln( 1- R)

  • Independent Variables
    • Deterministic, or
    • Fit a probability distribution, or
    • Use empirical distribution


  • Is the computer implementation of the conceptual model correct?
  • Procedures
    • Structured programming
    • Self-document
    • Peer-review
    • Consistency in input and output data
    • Use of IRC and animation


  • Can the conceptual model be substituted, at least approximately for the real system?
  • Procedures
    • Standing to criticism/Peer review (Turing)
    • Sensitivity analysis
    • Extreme-condition testing
    • Validation of Assumptions
    • Consistency checks


validation contd
Validation -contd.
  • Validating Input-Output transformations
  • Validating using historical input data


experimentation and output analysis
Experimentation and Output Analysis
  • Performance measures
  • Statistical Confidence
  • Run Length
  • Terminating and non-terminating systems.
  • Warm-up period.


system dynamics
System Dynamics
  • System
    • Collection of Interacting Elements working towards a Goal
  • System Elements
    • Entities
    • Activities
    • Resources
    • Controls
system dynamics contd
System Dynamics (contd.)
  • System Complexity
    • Interdependencies
    • Variability
  • System Performance Metrics
    • Flow (Cycle) Time
    • Utilization
    • Value-added Time and Waiting Time
    • Flow Rate
    • Inventory/Queue Levels
    • Yield
system dynamics contd22
System Dynamics (contd.)
  • System Variables
    • Decision Variables (Input Factors)
    • Response Variables (Output Variables)
    • State Variables
  • System Optimization
    • Finding the best combination of decision variables that minimizes/maximizes an objective function
system dynamics contd23
System Dynamics (contd)
  • Systems Engineering: The application of science and engineering to transform a need into a system with the following process:
    • Requirements definition
    • Functional analysis
    • Synthesis
    • Optimization
    • Design
    • Test
    • Evaluation
system dynamics contd24
System Dynamics (contd.)
  • Systems Analysis Techniques
    • Simulation
    • Hand Calculations
    • Spreadsheets
    • Operations Research Methods
      • Linear and Dynamic Programming
      • Queueing Theory (see Harrell p. 42-43)
simulation basics26
Simulation Basics
  • Types of Simulation
    • Static/ Dynamic
    • Stochastic/Deterministic
    • Discrete Event/Continuous
  • Simulating Random Behavior
    • Random Number Generation
    • Random Variate Generation
    • Probability Expressions and Distributions
simulation basics contd
Simulation Basics (contd.)
  • Workings of Discrete Event Simulation
    • Process Oriented World View
    • Sequence of Activities on Entities
    • Clock Advancement
    • Events: Scheduled and Conditional
simulation basics28
Simulation Basics
  • Example
    • Single-server queue
    • Arrival times uniformly distributed between 0.4 and 2 minutes. Mean arrival time = 1.2 minutes
    • Service time = 1 minute
    • Two Events: Arrival and Service completed
    • Simulation Table
discrete event simulation
Discrete Event Simulation
  • Modeling of a system as it evolves over time by a representation in which the state variables change instantaneously and only at separate (countable) points in time.
  • An EVENT is an instantaneous occurrence that may change the state of the system.
next event simulation clock advancement
Next-Event Simulation Clock Advancement
  • Clock initialized to zero
  • Schedule of future events determined
  • Clock advanced to the time of occurrence of the most-imminent event
  • System state updated
  • Time of occurrence of future events updated
  • Repeat until reaching termination event
components of a des model
Components of a DES model
  • System state
  • Simulation clock
  • Event list
  • Statistical counters
  • Initialization routine
  • Timing routine
  • Event routine
  • Library routine
  • Report generator
  • Main
simulation software
Simulation Software
  • Classification of Simulation Software
    • General-Purpose
    • Application-Oriented
  • Modeling Approaches
    • Event-scheduling approach
    • Process approach
simulation software contd
Simulation Software (contd)
  • Common Modeling Elements
    • Entities
    • Attributes
    • Resources
    • Queues
simulation software contd34
Simulation Software (contd)
  • Desirable Software Features
    • Modeling flexibility and ease of use
    • Hardware and software constraints
    • Animation
    • Statistical features
    • Customer support and documentation
    • Output reports and plots
des of a single server queue
DES of a Single Server Queue
  • M/M/1 queue
  • Mean interarrival time = 1 minute
  • Mean service time = 0.5 minutes
  • Find
    • Average time in queue? In system?
    • Average number in queue? In system
    • Server utilization?
    • Little’s formula?
simulation procedure
Simulation Procedure

Step 1: Define objective, scope, requirements

Step 2: Collect and analyze system data

Step 3: Build model

Step 4: Validate Model

Step 5: Conduct experiments

Step 6: Present results

Note: Iterations required among steps

definition of objective
Definition of Objective
  • Performance analysis
  • Capacity analysis
  • Configuration comparisons
  • Optimization
  • Sensitivity analysis
  • Visualization
definition of scope
Definition of Scope
  • Breadth (model scope)
  • Depth (level of detail)
  • Data gathering responsibilities
  • Planning the experimentation
  • Required format of results
definition of requirements
Definition of Requirements
  • The 90-10 rule
  • Size of project (data readily available)
    • small (2-4 weeks)
    • large (2-4 months)
  • Data gathering (50% of time)
  • Model building (20% of time)
simulation project steps
Simulation Project Steps

a.- Problem Definition

b.- Statement of Objectives

c.- Model Formulation and Planning

d.- Model Development and Data Collection

e.- Verification

f.- Validation


h.- Analysis of Results

i.- Reporting and Implementation

basic principles of modeling
Basic Principles of Modeling
  • To conceptualize a model use
    • System knowledge
    • Engineering judgement
    • Model-building tools
  • Remodel as needed
  • Regard modeling as an evolutionary process
manufacturing systems
Manufacturing Systems
  • Material Flow Systems
    • Assembly lines and Transfer lines
    • Flow shops and Job shops
    • Flexible Manufacturing Systems and Group Technology
  • Supporting Components
    • Setup and sequencing
    • Handling systems
    • Warehousing
characteristics of manufacturing systems
Physical layout




Work centers


Production Schedules

Production Control



Packing and Shipping

Characteristics ofManufacturing Systems
modeling material handling systems
Modeling Material Handling Systems
  • Up to 85% of the time of an item on the manufacturing floor is spent in material handling.
  • Subsystems
    • Conveyors
    • Transporters
    • Storage Systems
goals and performance measures
Goals and Performance Measures
  • Some relevant questions
    • How a new/modified system will work?
    • Will throughput be met?
    • What is the response time?
    • How resilient is the system?
    • How is congestion resolved?
    • What staffing is required?
    • What is the system capacity?
goals of manufacturing modeling
Goals of Manufacturing Modeling
  • Manufacturing Systems
    • Identify problem areas
    • Quantify system performance
  • Supporting Systems
    • Effects of changes in order profiles
    • Truck/trailer queueing
    • Effectiveness of materials handling
    • Recovery from surges
performance measures in manufacturing modeling
Performance Measuresin Manufacturing Modeling
  • Throughput under average and peak loads
  • Utilization of resources, labor and machines
  • Bottlenecks
  • Queueing
  • WIP storage needs
  • Staffing requirements
  • Effectiveness of scheduling and control
some key modeling issues
Some Key Modeling Issues
  • Alternatives for Modeling Downtimes and Failures
    • Ignore them
    • Do not model directly but adjust processing time accordingly
    • Use constant values for failure and repair times
    • Use statistical distributions
key modeling issues contd
Key Modeling Issues -contd
  • Time to failure
    • By wall clock time
    • By busy time
    • By number of cycles
    • By number of widgets
  • Time to repair
    • As a pure time delay
    • As wait time for a resource
key modeling issues contd53
Key Modeling Issues -contd
  • What to do with an item in the machine when machine downtime occurs?
    • Scrap
    • Rework
    • Resume processing after downtime
    • Complete processing before downtime
  • Single server resource with processing time exponential (mean = 7.5 minutes)
  • Interarrival time also exponential (mean = 10 minutes)
  • Time to failure, exponential (mean=100 min)
  • Repair time, exponential (mean 50 min)
example 5 1 contd
Example 5.1 -contd
  • Queue lengths for various cases
    • Breakdowns ignored
    • Service time increased to 8 min
    • Everything random
    • Random processing, deterministic breakdowns
    • Everything deterministic
    • Deterministic processing, random breakdowns
trace driven models
Trace Driven Models
  • Models driven by actual historical data
  • Examples
    • Actual orders for a sample of days
    • Actual product mix, quantities and sequencing
    • Actual time to failure and downtimes
    • Actual truck arrival times
a sampler of manufacturing models from wsc 98
A sampler of manufacturing models from WSC’98
  • Automotive
    • Final assembly conveyor systems
    • Mercedes-Benz AAV Production Facility
    • Machine controls for frame turnover system
a sampler of manufacturing models from wsc 98 contd
A sampler of manufacturing models from WSC’98 -contd
  • Assembly
    • Operational capacity planning: daily labor assignment in a customer-driven line at Ericsson
    • Optimal design of a final engine drop assembly station
    • Worker simulation
a sampler of manufacturing models from wsc 98 contd59
A sampler of manufacturing models from WSC’98 -contd
  • Scheduling
    • Batch loading and scheduling in heat treat furnace operations
    • Schedule evaluation in coffee manufacture
    • Manufacturing cell design
a sampler of manufacturing models from wsc 98 contd60
A sampler of manufacturing models from WSC’98 -contd
  • Semiconductor Manufacturing
    • Generic models of automated material handling systems at PRI Automation
    • Cycle time reduction schemes at Siemens
    • Bottleneck analysis and theory of constraints at Advanced Micro Devices
    • Work in process evolution after a breakdown
    • Targeted cycle time reduction and capital planning process at Seagate
a sampler of manufacturing models from wsc 98 contd61
A sampler of manufacturing models from WSC’98 -contd
  • Semiconductor Manufacturing - contd
    • Local modeling of trouble spots in a Siemens production facility
    • Validation and verification in a photolithography process model at Cirent
    • Environmental issues in filament winding composite manufacture
    • Order sequencing
a sampler of manufacturing models from wsc 98 contd62
A sampler of manufacturing models from WSC’98 -contd
  • Materials Handling
    • Controlled conveyor network with merging configuration at Seagate
    • Warehouse design at Intel
    • Transfer from warehouse to packing with Rapistan control system
    • Optimization of maintenance policies
manufacturing simulators


Taylor II



ModSim and Simprocess



Valisys (Tecnomatix)

Open Virtual Factory



Manufacturing Simulators