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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

1

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.

2

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.

3

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

4

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.

5

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.

6

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

7

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.

8

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

9

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

10

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

11

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

12

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

13

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

14

verification
Verification
  • 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

15

validation
Validation
  • 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

16

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

17

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

18

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

g.-Experimentation

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

Labor

Equipment

Maintenance

Work centers

Product

Production Schedules

Production Control

Supplies

Storage

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
example
Example
  • 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
ProModel

Witness

Taylor II

AutoMod

Arena

ModSim and Simprocess

SimSource

Deneb

Valisys (Tecnomatix)

Open Virtual Factory

EON

Simul8

Manufacturing Simulators