1 Modeling and Analysis of Manufacturing Systems Session 3 Simulation Models January 2001

2 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.

3 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.

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

5 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.

6 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.

7 Modeling Structures Process-Interaction Method Event-Scheduling Method Activity Scanning Three-Phase Method

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

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

10 Disadvantages of Simulation Training required. Interpretation of results required. Time consuming/expensive. Inappropriately used.

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

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

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

14 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

15 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

16 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

17 Validation -contd. –Validating Input-Output transformations –Validating using historical input data

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

System Dynamics and Simulation 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 –Spreadsheets –Operations Research Methods Linear and Dynamic Programming Queueing Theory (see Harrell p )

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?

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 Breadth (model scope) Depth (level of detail) Data gathering responsibilities Planning the experimentation Required format of results

Definition of Requirements The 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

Characteristics of Manufacturing Systems Physical layout Labor Equipment Maintenance Work centers Product Production Schedules Production Control Supplies Storage Packing and Shipping

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 Measures in 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

Manufacturing Simulators ProModel Witness Taylor II AutoMod Arena ModSim and Simprocess SimSource Deneb Valisys (Tecnomatix) Open Virtual Factory EON Simul8