G54SIM Simulation for Computer Scientists - Lec01 (2010)

8/7/2019 G54SIM Simulation for Computer Scientists - Lec01 (2010)

1/33

G54SIM

Lecture 01

Introduction to Modelling and Simulation

Peer-Olaf Siebers

[email protected]


2/33


3/33

Lecture Outline

1. Module Organisation

2. Simulation Examples

3. Systems

4. Models5. Simulation

6. Why Simulate?

7. Classification of Simulation

8. Level of Abstraction

9. Paradigms

G54SIM (http://www.cs.nott.ac.uk/~pos/g54sim/) 3


4/33

G54SIM (http://www.cs.nott.ac.uk/~pos/g54sim/)

Module Organisation

Classes: Fridays, 4-6pm, BSSouth-A24

Labs: Tuesday, 4-6pm, CompSci-A32

Contact: CompSci-B36,

Appointment via email ([email protected])

http://www.cs.nott.ac.uk/~pos/g54sim/

Assessment:

60%: Written examination

40% : Simulation case study and report

4
mailto:[email protected]://www.cs.nott.ac.uk/~pos/g54sim/http://www.cs.nott.ac.uk/~pos/g54sim/mailto:[email protected]


5/33


Module Organisation

Recommended reading:

There is no course book that covers all topics!

Robinson (2004). Simulation: The Practice of Model Development and Use

Gilbert and Troitzsch (2005). Simulation for the Social Scientist. 2nd Ed

WSC 2009 Proceedings: Introductory Tutorials

Course software: AnyLogic (xjTek)

Requires some basic Java programming skills

G54SIM Lab 2 (5th October): Introduction to Java for AnyLogic Sierra and Bates (2009). Head First Java. 2nd Ed

YouTube: The New Boston Channel - Java Programming Tutorials

5


6/33


Module Organisation

Topics covered in this module

General principles of simulation

Modelling and simulation methods

Building a valid simulation model

Input modelling

Experimental design

Output analysis

Case studies

6


7/33


Simulation Examples

7


8/33


Simulation Examples

IMA Simulation Work

Simulating Retail Management Practices

Investigating the impact of human resource management practices on

customer satisfaction

Modelling and Analysing Cargo Screening Processes

Optimising the process flow of the cargo screening process

Using simulation for cost-benefit analysis (risk assessment)

Future Energy Decision Making for Cities

Modelling Energy Consumption Patterns

Testing governmental intervention strategies

8


9/33

9

Systems

System:

Collection of parts organised for some purpose (weather system:

parts: sun, water, land, etc.; purpose: maintaining life)

Defining a system requires setting boundaries

Different categories of systems:

Natural systems (weather system, galactic system)

Designed physical systems (house, car, production system)

Designed abstract systems (mathematics, literature)

Human activity systems (family, city, political system)

9G54SIM (http://www.cs.nott.ac.uk/~pos/g54sim/)


10/33


Systems

System:

Collection of parts organised for some purpose (weather system:

parts: sun, water, land, etc.; purpose: maintaining life)

Defining a system requires setting boundaries

Different categories of systems:

Natural systems (weather system, galactic system)

Designed physical systems (house, car, production system)

Designed abstract systems (mathematics, literature)

Human activity systems (family, city, political system)

10


11/33

11

Systems

Operations systems: Configuration of resources combined for

the provision of goods and services (functions: manufacture,

transport, supply, service)

Social systems: Entities or groups in definite relation to each

other which create enduring patterns of behavior and

relationship within social systems.

Economic system: Is a particular set of social institutions

which deals with the production, distribution and

consumption of goods and services in a particular society.



12/33


Systems

Operations systems: Configuration of resources combined for

the provision of goods and services (functions: manufacture,

transport, supply, service)

Social systems: Entities or groups in definite relation to each

other which create enduring patterns of behavior and

relationship within social systems.

Economic system: Is a particular set of social institutions

which deals with the production, distribution and

consumption of goods and services in a particular society.

12


13/33


Models

Model:

Some form of abstract representation of a real system intended to

promote understanding of the system it represents.

A model is a static representation of the system

Models can have many forms (e.g. mathematical equations, diagrams,physical mock-ups)

Why model?

Models give us a comprehensible representations of a systems Something to think about

Something to communicate about

13


14/33


Simulation

Simulation:

The process of designing a model of a real system and conducting

experiments with this model for the purpose of understanding the

behavior of the system and /or evaluating various strategies for the

operation of the system Uses a model to emulate the dynamic characteristics of a system

Nature of simulation model use:

Predict the performance of a system under a specific set of inputs Experimental approach to modelling (What-if analysis tool)

14


15/33


Why Simulate?

Operations systems are subject to variability

Predictable variability

(e.g. staff rota, planned maintenance of machines)

Unpredictable variability

(e.g. customer arrivals, machine breakdowns)

Operations systems are interconnected

Components of a system affect one another

(e.g. customers in a three stage service process)

15


16/33


Why Simulate?

Operations systems are complex

Combinatorial complexity

(number of components and number of combinations of components)

Dynamic complexity

(mainly systems that are highly interconnected; feedback systems; actionhas different effect in short/long run; action has different consequences in

one part of the system compared to another; action has non-obvious

consequences)

Simulation models are able explicitly to represent thevariability, interconnectedness, and complexity of a system

16


17/33

17

Break

See you back in 10 minutes



18/33

Lecture Outline

1. Module Organisation

2. Simulation Examples

3. Systems

4. Models5. Simulation

6. Why Simulate?

7. Classification of Simulation

8. Level of Abstraction

9. Paradigms



19/33


Why Simulate?

It is possible with a simulation:

to predict system performance

to compare alternative system designs

to determine the effects of alternative policies on system performance

Advantages: Simulation vs. experimentation

Cost

Time (real time vs. virtual time)

Control of experimental conditions If real system does not exist

19


20/33


Why Simulate?

Advantages: Simulation vs. other modelling approaches:

Modelling variability (some other approaches could be adapted to

account for variability but it often increases their complexity)

Restrictive assumptions (most of the other approaches require

assumptions, e.g. queuing theory assumes particular distributions forarrival and service times, for many processes these distributions are

not appropriate)

Transparency (more intuitive than a set of equations, an animated

display of the system can be created, giving a non-expert grater

understanding of, and confidence in, the model) Creating knowledge and understanding (sometimes just building the

model is enough)

Visualisation, communication, interaction

20


21/33


Why Simulate?

Disadvantages of simulation:

Expensive

Time consuming

Data hungry

Requires expertise (its an art rather than a science)

Overconfidence (when interpreting the results from a simulation,

consideration must be given to the validity of the underlying model

and the assumption and simplifications that have been made!)

21


22/33

22

Classification of Simulation

Static vs. Dynamic:

Static: No attempts to model a time sequence of changes.

Dynamic: Updating each entity at each occurring event.

Deterministic vs. Stochastic:

Deterministic: Rule based.

Stochastic: Based on conditional probabilities.

Discrete vs. Continuous:

Discrete: Changes in the state of the system occur instantaneously at

random points in time as a result of the occurrence of discrete events. Continuous: Changes of the state of the system occur continuously

over time.



23/33


Classification of Simulation

Static vs. Dynamic:

Static: No attempts to model a time sequence of changes.

Dynamic: Updating each entity at each occurring event.

Deterministic vs. Stochastic:

Deterministic: Rule based.

Stochastic: Based on conditional probabilities.

Discrete vs. Continuous:

Discrete: Changes in the state of the system occur instantaneously at

random points in time as a result of the occurrence of discrete events. Continuous: Changes of the state of the system occur continuously

over time.

23


24/33


Level of Abstraction

Simulation can be applied at different stages:

Strategic

high abstraction, less detailed, macro level

Tactical

middle abstraction, medium details, meso level

Operational

low abstraction, more details, micro level

24


25/33



Strategic

Tactical

Operational

Aggregate, global causal dependencies, feedback dynamics

Individual objects, exact sizes, velocities, distances, timings

traffic macro modelling, traffic micro modelling,supply chain management, population dynamics,

financial risk analysis, manufacturing systems,ecosystems, pedestrian dynamics, health care

applications, health economics, military planning,business dynamics, warehouse operations

25


26/33



traffic macro

modelling

traffic micro

modelling

supply chain

management

population

dynamics

financial risk

analysis

queuing

systems

ecosystems

pedestrian

dynamics

health care

applications

health

economics

military

planning

business

dynamicsStrategic

Tactical

Operational



warehouse

operations

26


27/33


Paradigms

System Dynamics Modelling (SDM) and Simulation (SDS)

Modelling: Causal loop diagrams

Simulation: Deterministic continuous (differential equations)

Discrete Event Modelling (DEM) and Simulation (DES)

Modelling: Flow charts

Simulation: Stochastic discrete (flow oriented approach)

Agent Based Modelling (ABM) and Simulation (ABS)

Modelling: State charts

Simulation: Stochastic discrete (object oriented approach)

Mixed Method Modelling (MMM) and Simulation (MMS)

27


28/33

28

Paradigms

Strategic

Tactical

Operational





29/33

29

Paradigms

Strategic

Tactical

Operational



DiscreteEvent

AgentBased

System

Dynamics



30/33


Summary and Outlook

Summary

Definition of systems, models, and simulation

Why simulate

Classification of simulation

Level of abstraction

Paradigms

Outlook

How to conduct simulation studies

30


31/33


Further Reading

Robinson S (2004). Simulation: The Practice of Model Development and

Use. Chapter 1

Borshchev A and Filippov A (2004). From System Dynamics and Discrete

Event to Practical Agent Based Modeling: Reasons, Techniques, Tools.http://www.xjtek.com/anylogic/articles/33/

31


32/33

Questions / Comments



33/33

G54SIM (http://www cs nott ac uk/~pos/g54sim/)

References

Shannon R E (1975). Systems Simulation: The Art and Science. Prentice-

Hall: Englewood Cliffs, NJ.

33

Date post:	08-Apr-2018
Category:	Documents
Upload:	peer-olaf-siebers
View:	216 times
Download:	0 times

G54SIM Simulation for Computer Scientists - Lec01 (2010)

Documents