into to simulation

16
HAKEEMURREHMAN PhD (Scholar) Management Science & Engineering Center of Logistics & Operations Management, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China 1 Simulation Modeling: An Overview

Upload: hakeem-ur-rehman

Post on 14-Jan-2017

51 views

Category:

Education


0 download

TRANSCRIPT

Page 1: Into to simulation

HAKEEM–UR–REHMANPhD (Scholar) Management Science & Engineering

Center of Logistics & Operations Management,

Antai College of Economics and Management,

Shanghai Jiao Tong University, Shanghai, China1

Simulation Modeling:

An Overview

Page 2: Into to simulation

Outlines

2

What is a System?

Components of a System

Types of Systems

Model of a system

Simulation & Simulation model types

Monte Carlo Simulation: Example

Discrete Event Simulation: Example

Page 3: Into to simulation

What is a System?

3

A system is any set of interrelated components acting together to

achieve a common objective.

INPUT OUTPUTSYSTEM

Manufacturing

Healthcare

Banking

Page 4: Into to simulation

Components of a System

4

Entity: “Flow units transformed by the system over time”

Attribute: “A property of an entity”

Activity: “Time period of specified Length”

o Example: Banking System

o Customers might be one of the entities

o The balance in their checking accounts might be an attribute

o Making deposits might be an activity

Sate of a System: “Collation of variables necessary to describe the

system at any time relative to the objectives of the study”

o Bank: # of busy tellers, # of customers waiting in the queue,

arrival time of the next customer”

Page 5: Into to simulation

Three Types of Systems

5

System

Quantum System

Subatomic World

Cosmological Systems

Continuous System

Electro-mechanical

Systems

Socio-economic, Ecological Systems

Discrete–Event System

Industrial Systems

(Factory, Office, etc.)

Components of a system are

described using quantum

mechanics

physical dynamics are

described using differential

equations of effort,

such as force and voltage, and

flow, such as velocity and

current

event-driven system

an instance of

changes in state

variables

Page 6: Into to simulation

Model of a System

6

A model is a representation of the structure of a real life system.

REAL

SYSTEM

o Simplification

o Abstraction

o Assumptions

MODEL

System

Page 7: Into to simulation

What is a Simulation?

7

The process of running a computer model of a real system to study

or conduct experiments

For understanding the model or its behavior

To evaluate strategies for operation of the system

used to draw conclusions about the real system.

Simulation

vs.

Real World

Page 8: Into to simulation

Different Kinds of Simulation

8

Continuous Vs. Discrete Change

Can “state” change continuously,

or only at discrete points in time?

Deterministic Vs. Stochastic

Is everything for sure or is there

uncertainty?

Static Vs. Dynamic

Does time have a role in model?

Most operational models: Stochastic, Dynamic, Discrete-change

System Model

Deterministic Stochastic

Static Dynamic Static Dynamic

Continuous Discrete Continuous Discrete

Monte Carlo

Simulation

Continuous

SimulationContinuous

Simulation

Discrete

Event

Simulation

Discrete

Event

Simulation

Page 9: Into to simulation

Monte Carlo Simulation

9

Monte Carlo methods (or Monte Carlo experiments) are a broad

class of computational algorithms that rely on repeated random

sampling to obtain numerical results.

“Monte Carlo is a method of approximating things using samples”

Monte Carlo: approximates expectations with a sample average

𝐴𝑝𝑝𝑟𝑜𝑥𝑖𝑚𝑎𝑡𝑒 𝐸 𝑓 𝑥 = 𝑓 𝑥 𝑃 𝑥 𝑑𝑥 ≈1

𝑆 𝑠=1

𝑆 𝑓 𝑥 𝑠 𝑥 𝑠 ~𝑃 𝑥

ACCEPTANCE–REJECTION SAMPLING:

Sampling Underneath a 𝑃 𝑥 ∝ 𝑃(𝑥) curve is also

valid

o Draw underneath a simple curve 𝑘 𝑄(𝑥) ≥ 𝑃(𝑥)• Draw 𝑥 ~𝑄(𝑥)• Height 𝑢 ~𝑈𝑛𝑖𝑓𝑜𝑟𝑚[0, 𝑘 𝑄(𝑥)]

o Discard the point if above 𝑃 𝑥 ,

(i.e. if 𝑢 > 𝑃(𝑥))

Page 10: Into to simulation

Monte Carlo Simulation: Example

10

Using MC to Estimate 𝜋

𝐴𝑟𝑒𝑎 𝑜𝑓 𝐶𝑖𝑟𝑐𝑙𝑒 = 𝜋𝑟2

𝐴𝑟𝑒𝑎 𝑜𝑓 𝑆𝑞𝑢𝑎𝑟𝑒 = 4𝑟2

𝑥2 + 𝑦2 = 𝑟2

𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑡𝑦 𝑝𝑜𝑖𝑛𝑡 𝑖𝑛𝑠𝑖𝑑𝑒 𝑡ℎ𝑒 𝑐𝑖𝑟𝑐𝑙𝑒 =𝜋𝑟2

4𝑟2

𝝅 = 4 ∗ 𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑡𝑦 𝑝𝑜𝑖𝑛𝑡 𝑖𝑛𝑠𝑖𝑑𝑒 𝑡ℎ𝑒 𝑐𝑖𝑟𝑐𝑙𝑒

Page 11: Into to simulation

Discrete Event Simulation

11

A single Machine system

Simulation Model Trajectory

Also called System Simulation

“process of codifying the behavior of a complex system as an

ordered sequence of well-defined events”

Page 12: Into to simulation

Discrete Event Simulation…

12

Collecting Statistics from the Model Trajectory:

Queue Length 𝑞(𝑡) statistics during 𝑡 ∈ [𝑡0, 𝑡10] Average waiting time

Average System time (Waiting time + Service time)

Resource utilization

Page 13: Into to simulation

Discrete Event Simulation…

13

Arena Simulation Software

https://www.arenasimulation.com/academic/students

A Single Machine System

Entity: Job arrives every 𝑡𝑎 minutes (𝑡𝑎~𝐸𝑥𝑝(5)) Active Resource: Machine

Passive Resource: Buffer (unlimited)

Activity: service time 𝑡𝑠~𝑇𝑟𝑖𝑎𝑛𝑔𝑢𝑙𝑎𝑟(1,3,6))

Example

Page 14: Into to simulation

Discrete Event Simulation…

14

Page 15: Into to simulation

Books

15

For Theory + AnalysisFor Arena Software

Page 16: Into to simulation

16