Modeling and Analysis of Manufacturing Systems

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# Modeling and Analysis of Manufacturing Systems - PowerPoint PPT Presentation

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

Session 3

Simulation Models

January 2001

1

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

4

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
• 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
• Process-Interaction Method
• Event-Scheduling Method
• Activity Scanning
• Three-Phase Method

7

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

8

• Building consensus.
• Preparing for change.
• Cost effective investment.
• Training aid capability.
• Specification of requirements.

9

• Training required.
• Interpretation of results required.
• Time consuming/expensive.
• Inappropriately used.

10

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

11

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

12

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

13

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
• 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
• 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.
• Validating Input-Output transformations
• Validating using historical input data

17

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

18

### System Dynamics andSimulation Basics

System Dynamics
• System
• Collection of Interacting Elements working towards a Goal
• System Elements
• Entities
• Activities
• Resources
• Controls
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 (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 (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 (contd.)
• Systems Analysis Techniques
• Simulation
• Hand Calculations
• Operations Research Methods
• Linear and Dynamic Programming
• Queueing Theory (see Harrell p. 42-43)

### Simulation Basics

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.)
• Workings of Discrete Event Simulation
• Process Oriented World View
• Sequence of Activities on Entities
• Events: Scheduled and Conditional
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
• 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.
• 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
• System state
• Simulation clock
• Event list
• Statistical counters
• Initialization routine
• Timing routine
• Event routine
• Library routine
• Report generator
• Main
Simulation Software
• Classification of Simulation Software
• General-Purpose
• Application-Oriented
• Modeling Approaches
• Event-scheduling approach
• Process approach
Simulation Software (contd)
• Common Modeling Elements
• Entities
• Attributes
• Resources
• Queues
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
• 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?

### Getting Started

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
• Performance analysis
• Capacity analysis
• Configuration comparisons
• Optimization
• Sensitivity analysis
• Visualization
Definition of Scope
• Depth (level of detail)
• Data gathering responsibilities
• Planning the experimentation
• Required format of results
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)

### The Simulation Project

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
• To conceptualize a model use
• System knowledge
• Engineering judgement
• Model-building tools
• Remodel as needed
• Regard modeling as an evolutionary process

### Manufacturing Systems Simulation

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
Physical layout

Labor

Equipment

Maintenance

Work centers

Product

Production Schedules

Production Control

Supplies

Storage

Packing and Shipping

Characteristics ofManufacturing 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
• 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
• 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 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
• 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
• 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 -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
• 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
• 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
• 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
• 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
• 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 -contd
• Scheduling
• Schedule evaluation in coffee manufacture
• Manufacturing cell design
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 -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 -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
ProModel

Witness

Taylor II

AutoMod

Arena

ModSim and Simprocess

SimSource

Deneb

Valisys (Tecnomatix)

Open Virtual Factory

EON

Simul8

Manufacturing Simulators