Modeling and Analysis of Manufacturing Systems

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

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. 2

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. 3

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

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. 5

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. 6

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

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. 8

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

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

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

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

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

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 14

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 15

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 16

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

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

19. System Dynamics andSimulation Basics

20. System Dynamics • System • Collection of Interacting Elements working towards a Goal • System Elements • Entities • Activities • Resources • Controls

21. 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

22. 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

23. 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

24. System Dynamics (contd.) • Systems Analysis Techniques • Simulation • Hand Calculations • Spreadsheets • Operations Research Methods • Linear and Dynamic Programming • Queueing Theory (see Harrell p. 42-43)

25. Simulation Basics

26. 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

27. Simulation Basics (contd.) • Workings of Discrete Event Simulation • Process Oriented World View • Sequence of Activities on Entities • Clock Advancement • Events: Scheduled and Conditional

28. 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

29. 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.

30. 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

31. Components of a DES model • System state • Simulation clock • Event list • Statistical counters • Initialization routine • Timing routine • Event routine • Library routine • Report generator • Main

32. Simulation Software • Classification of Simulation Software • General-Purpose • Application-Oriented • Modeling Approaches • Event-scheduling approach • Process approach

33. Simulation Software (contd) • Common Modeling Elements • Entities • Attributes • Resources • Queues

34. 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

35. 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?

36. Getting Started

37. 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

38. Definition of Objective • Performance analysis • Capacity analysis • Configuration comparisons • Optimization • Sensitivity analysis • Visualization

39. Definition of Scope • Breadth (model scope) • Depth (level of detail) • Data gathering responsibilities • Planning the experimentation • Required format of results

40. 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)

41. The Simulation Project

42. 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

43. 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

44. Manufacturing Systems Simulation

45. 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

46. Physical layout Labor Equipment Maintenance Work centers Product Production Schedules Production Control Supplies Storage Packing and Shipping Characteristics ofManufacturing Systems

47. 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

48. 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?

49. 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

50. 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