IE 415/515 – Simulation

Today’s AgendaInformation on syllabus Office hours Prerequisites Text Grading Exams & Homework Class format

Introduction to IE simulationContinue lecture material

Office Hours

Mondays/Wednesdays 3-4:30PM By appointmentOffice 424 RogersE-mail: No HW/technical questions! TA: Faisal Alfayez, Zahra Mohktari Office hours TBD

PrerequisitesStat 314 or equivalent will be needed. Computer programming experience – Helpful but not critical. If specific material is needed, it will be covered for course purposes. Knowledge of Windows and Excel is assumed. Experience programming and debugging is helpful.Some ENGR 390 (engineering economics) background.

Course InformationCourse homepage :

http://classes.engr.oregonstate.edu/mime/winter2015/ie415-001 Syllabus Lecture slides for note taking Handouts

This introductory presentation Information sheet

Homework and lab assignments Check the page for course

information and announcements.

ReferencesKelton, W.D., Sadowski, R.P. and N.B. Swets, (2010). Simulation With Arena 5th Edition, McGraw-Hill Inc.

Valley Library -11 copies on one-day reserve.

Law, A.M. and W.D. Kelton, (2000). Simulation Modeling and Analysis 3rd Edition, McGraw-Hill Inc.

Two earlier editions on 3-hour reserve

Banks, J., Carson, J.S., Nelson, B.L., Nicol, D.M., (2010). Discrete Event System Simulation 5th Edition, Prentice Hall. On Reserve.Arena online books.Crystal Ball online documentation.

Grading – Allocation

Class participation based on:1. Participation in class – answering questions2. In-class exercises3. Random attendance taken4. Submitting information sheet

Mid-term Exam (2/15/2015) 25%

Final Exam (Monday 3/16/2015 12:00-1:50PM)

35%

Lab Exams (Week 4 lab, Week 9 lab) 20%

Homework/Lab Assignments

(12-14 Labs/HW)15%

Class Participation (includes information sheet if applicable)

5%

Grading Scale92 or above A

89-92 A-

86-89 B+

82-86 B

79-82 B-

76-79 C+

72-76 C

69-72 C-

59-69 D

Exams, Homework, LabsHomework 5-7 homework assignments will be given. Some solutions for programming problems

will be provided after assignment is turned in.

Group study is encouraged but each person should understand all problems.

Due at the beginning of class – Late HW penalized After the final call for homework – 2 out of 10. After 12 noon the day immediately following the

final call – assignment not accepted.

Exams, Homework, LabsLabs Labs start in week 1. Seven total lab assignments – No labs in

week 10. Due by the end of lab. Counts the same as a HW assignment.

Switching lab sections Only with prior approval of the TAs or

instructor. The number approved requests for

switching sections will be limited.

Exams, Homework, LabsLab exams Two lab exams: week 4 and week 9. Tests simulation modeling with specific

software. Graded by the TAs. Different sections will be given different

versions of the exam. The paper copies of the exam are to be returned

to the TAs. No photographs of the exam are allowed.

Exams, Homework, LabsIn-class Exams

Open book and open note exams – No laptop computers, tablets, smartphones, etc. and no electronic communication permitted.

Based on homework, lecture material (in-class exercises and examples), labs.

Exams will only be distributed in class and in office hours for viewing and will then be returned to the instructor. No photos of the exam are allowed.

Grading questions/modifications must be brought to the instructor within one week after the exam is returned in class.

Recipe for FailureLow effort on HW

Utilize solutions from prior terms Split problems with classmates Turn in late HW

Low effort on labs Rely on your partner to complete lab Focus on procedures instead of what the procedure

accomplishes

Don’t attend class Physically Mentally

Things To DoDo the opposite of Recipe for FailureGet your points

Get your HW and lab points At some point do the HW and labs with good effort

Seek help early in the term if neededDo problems under a time constraint

IE 415 vs. IE 515

IE 415/515 differences IE 515 – additional homework (may

require study outside of class material)

One or more questions on each exam will differ

Grading will be harder for 515 students

Lecture FormatThe first part of class will be devoted to questions.

Unreasonably long questions will be handled one on one.

Lecture Ask questions

End of Class – Will try to leave time for questions.

Lecture FormatMaterial will be delivered on slides using a tablet PC. Material will be added to the slides during class. Examples will be completed electronically on

the slides. There will be periodic in-class problem solving

sessions. Solutions completed electronically on slides.

Minor changes to the slides may be made just before class.

All added (hand written) material is your responsibility – They will not be available on the website.

18

Class RulesTurn off/quiet cell & smart phones and other communication devices.No web surfing.No newspapers.No completing homework or other assignments.No sleeping.Use common sense and be considerate of others.

Questions ?

IE 415/515 - Introduction

Simulation

Example 1As an IE working at a manufacturing plant, you are asked to help evaluate a potential investment in a new machine (at a highly utilized process step). The company has a number of different types of jobs that undergo processing at this step. Currently there are five machines, and each machine can only process a subset of the jobs. Each existing machine also experiences random failures. The new machine can process any of the currently produced jobs. Jobs arrive in batches (each batch with different job types of known composition). Assuming the percentages of different job types remains the same, you are asked to evaluate the increased throughput realizable by purchasing this machine.

.

.

.

A,B

C,D

J,K

Jobs to be processed Completed jobs at rate?

New Machine – All job types

Example 2Applying engineering economic analysis to evaluate the NPV of two alternatives for fork lift purchases.In addition to NPV, evaluate each alternative with respect to cost risk/uncertainty. There is uncertainty in many of the

parameters used in these calculations.

Example 2Known parameters Initial costs Approximate fuel costs (e.g., gas vs.

electric) in the near future.

Unknown parameters Breakdown/maintenance costs Salvage/resale value Future fuel costs

Example

How do you proceed?

ApproachesExperience/Intuition Often effective but limited in very complex

situationsAnalytical models – Mathematical equations Usually preferred if available Usually very fast – many types of “what if” Provide insight into key parameters Limited availability/accessibility

Computer simulations Applicable to the most complex situations

given enough time

Simulation Questions

How do you simulate these systems? What software choices? How are system dynamics represented/simulated?

How do you represent randomness in the system?What is the form of the answer?

How do you interpret simulation results?

What data needs to be collected? How is the data processed?

…

Simulation

Dictionary definition – “to look or act like”Almost everything done in engineering is simulationEngineers build models to predict and understand the performance of all types of things, systems, and processes

Examples

Equations predicting what happens in physical systems – thermodynamics, statics, …Physical prototypes of products for development, test and validationFinal product testingProcess validation – Soft toolingFlight simulatorsArcade games

Types of Systems IEs SimulateThey are big and costlyInvolve peopleRandom events/values occur over timeThe systems are too big to build physical prototypesA calculation may involve the combination of multiple random componentsThe systems may not exist

Systems IEs Simulate - Examples

Production line performanceCall centers performancePlant floor layout – material movementScheduling of resourcesNetwork performanceInventory control/ordering pointsDistribution and routingEngineering economic calculations incorporating randomness…

Characteristics of Systems IEs Simulate

System operation is often dictated by man-made rules, or the focus is on establishing efficient rulesExamples

Staffing for a desired level of customer performance.

Sizing/allocation of storage areas. The number of machines to use at a

workstation. The scheduling of work. Etc.

IE Computer SimulationsIn practice, simulation refers to the process of designing and creating computerized models of a system and doing numerical computer-based experiments. Real power - application to complex systems.Industry acceptance.

Objectives of IE Analysis

Estimate performance Throughput of a production line. Average wait time for customers. Minimum investment to achieve a target. Distribution of NPV values. …

Evaluate designs Plant layouts Scheduling rules Production system configurations …

IE Computer Simulations - Types

Deterministic/StochasticDiscrete/Continuous stateStatic/DynamicIE 415/515 will focus on Stochastic, Discrete, Static & Dynamic simulations.

Example

Expected value (average) of the max value from two rolls of a die Approaches

Experience/intuition Analytical Simulation

Physical Computer simulation

Example

Experience/Intuition

Example – Analytical ModelExpected value (average) of the max value from two rolls of a die

Analytical (can also enumerate for this example).

6

1

2

21

2

121

121

6

1

12121

47.412

42

6

1)],[max(

12

42

)6

1(*2

)1(6)

6*()]|,[max(

)]|,[max(*6

1

)]]|,[max([)],[max(

i

i

iiXXE

ii

iiiiiXXXE

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XXXEEXXE

Simulation

Expected value (average) of the max value from two rolls of a die Physical simulation

Example

Expected value (average) of the max value from two rolls of a die Computer simulation

ExampleComputer simulation answer is not a single value More work – more precision

95 % Confidence Intervals for the Avg.150 Trials

Half Width Low Limit Upper Limit

0.23292914 4.12707086 4.592929141

1500 Trials

Half Width Low Limit Upper Limit

0.07068044 4.34931956 4.49068044

Static Stochastic Simulation

Spreadsheet packages @Risk Crystal Ball

Dynamic Stochastic Simulation

The passing of time is a fundamental part of the simulation. For IEs this time is normally the time

a system (e.g., a plant) is operating.

Dynamic stochastic simulations are often animated Validation Communication

Example

M/M/1 Queuing System Avg. # in queue, Avg. time in system

Example

Experience/Intuition

Example – Analytical Model

M/M/1 Queuing System Many results have been obtained.

Example – Physical Simulation

M/M/1 Queuing System Most likely not possible – Instead, the

real system can be observed.

Example – Computer Simulation

M/M/1 Queuing System Arena software utilized.

Dynamic Stochastic Simulations

Physical simulations too costly or not possible.Analytical models do not exist - System is too complex.Demo

Dynamic Stochastic Simulations

Normally executed with “simulation software” General-purpose languages (C++)

Tedious, low-level, error-prone Almost complete flexibility Can be used to program static stochastic

simulations too Support packages

Subroutines for list processing, bookkeeping, time advance

Widely distributed, widely modified

Dynamic Stochastic Simulations

Simulation languages GPSS, SIMSCRIPT, SLAM, SIMAN Learning curve for features, effective use,

syntax

High-level simulators Very easy, graphical interface Domain-restricted (manufacturing,

communications) Limited flexibility — model validity?

Dynamic Stochastic Simulations

Demo

Warnings

Simulation is very time consuming Model development Data collection This often makes simulation infeasible

Simulations are complicated – Easy to make errors (logical), validation is often difficultGarbage in – Garbage outSimulation output has randomness

Goals of this Course

Students successfully completing this course should (independent of the simulation system): Understand the basic mechanics of how

almost all discrete event simulation systems operate

Be able to carry out a “complete” simulation project

Understand pros and cons of using simulation to study dynamic systems

Simulation Coverage Outline

Start with static stochastic simulations Probability and statistics required Cover fundamentals in lecture We will use Excel/Crystal Ball Software in

the lab

Move to discrete dynamic stochastic simulations Cover fundamentals in lecture We will use Arena software in the lab