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## PowerPoint Slideshow about 'Modeling and Analysis of Manufacturing Systems' - Mia_John

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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

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

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.

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

9

Disadvantages of Simulation

- 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

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

Experimentation and Output Analysis

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

18

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
- Spreadsheets
- Operations Research Methods
- Linear and Dynamic Programming
- Queueing Theory (see Harrell p. 42-43)

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
- Clock Advancement
- 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.

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

- 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?

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

- Breadth (model 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)

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

- 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 SystemsModeling 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
- 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 -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
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