Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Advanced Business Analytics > 9 - Simulation Modelling > Flashcards

9 - Simulation Modelling Flashcards

1
Q

What is a simulation and why do we need it?

Simulation

A

Something that is made to look, feel or behave like something else, so it can be studied or used to train people

Simulation is reproducing a dynamic process in a system using a model that lends itself to experimentation, to achieve insights that can be transferred to the real world

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What is a simulation and why do we need it?

Simulation vs. real world

A
  • simulation allows us to test new strategies and future scenarios in complex systems ceteris paribus
  • the goal are not mathematically optimal solutions but a deeper understanding of the system behavior and influences
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What is a simulation and why do we need it?

Simulation vs. real world

The paradigms

A
  • discrete-event-based
  • agent based (how do we look at decision making in the system?)
  • system dynamics (continuous vs. discrete changes)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What is a simulation and why do we need it?

Motivation: Simulation …

A
  • supports the understanding of complex relationships in dynamic systems
  • can use an artificial model to predict the real system’s behavior
  • can highlight bottlenecks and weaknesses in the real system
  • can test “intuitions” objectively
  • does not put experimental stress on the real system
  • can support strategic and operational decisions (by aiding the understanding of the system, not finding the optimal solution)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What is a simulation and why do we need it?

When not to use a simulation?

A

If experiments are easily and cheaply realized in the real systems
-> e.g. food testing in the super market

If the desired indicators can be calculated analytically

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Discrete-event simulation

A
  • Consider a modeled system as dynamic over time to be characterized by its state
  • over time, events occur at particular time points and change the system state
  • the simulation control processes events in the order of their occurrence and thereby moves through simulated time; this means that the simulated time does not pass smoothly
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Discrete-event simulation

Some concepts of discrete event simulation

Entity

A

Permanent:
stays in the system (resource, e.g. machine)

Temporary:
moves through the system (e.g. product)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Discrete-event simulation

Some concepts of discrete event simulation

Entity Queue

A
  • list of temporary entities with status waiting for an interaction
  • usually assigned to resources
  • processing can be FIFO, LIFO, random, …
  • processing may depend on entity characteristics (priority queue)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Discrete-event simulation

Some concepts of discrete event simulation

Events

A
  • mile stones in a discrete system
  • events describe all system changes
  • between events, the system state remains constant
  • an event does not use up time - it takes place at a single point in time
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Discrete-event simulation

Some concepts of discrete event simulation

Event list

A

List of all events with their respective time of occurrence

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Discrete-event simulation

Some concepts of discrete event simulation

Simulation clock

A

Variable Stating the current time in the simulation model

-> How much time was spent waiting for something to happen?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Discrete-event simulation

Some concepts of discrete event simulation

Statistics indicator

A
  • Variable storing statistic information about the system

- Example: Mean turn-over rate

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Discrete-event simulation

Some concepts of discrete event simulation

Entity state

A

Entity is busy:
- machine is producing/a work station is being used

Entity is idle (before or after activity):
- machine is available, a customer awaits processing in a queue

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Agent-based simulations

What is an agent-based simulation?

A
  • agent-based simulations explicitly model decision makers as agents

Agents can:

  • communicate, e.g. queuing up with friends
  • perceive system states
  • act rationally - or not, e.g. go to the shortest queue vs. go to the cashier who looks nice
  • influence system states
  • evolve
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Agent-based simulations

What are agents?

A
  • usually large sets of interconnected heterogenous agents are modeled to generate emergent effects
  • > form patterns (can be observed) = emergent effects
  • agent-based simulations are usually modeled on discrete time concepts
  • > they can be event-based
  • > they can be combined with continuously modeled environments
  • the ODD Protocol was designed to make agent-based simulations replicable
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

System-dynamic simulations

A

System-dynamic simulations qualitatively model effects of continuous influence factors

  • holistic approach of systems, integrating many subsystems
  • focuses on policies and system structure
  • Feedback loops to represent the effects of policy decisions
  • dynamic view of the cause and effect relationships among system elements
  • minimal data requirements to build a model

Originally from mechatronics, modeling continuous processes through differential equations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

How to build a model

Conflict

A

When modeling the desire for highly detailed models and minimal development costs conflict

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

How to build a model

Ways to derive a model from the real system

A

Reduction:
- abandonment of unimportant system components

Abstraction:
- generalization of specific system characteristics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

How to build a model

Solution to the conflict

A

Model as detailed as necessary, but as simple as possible, because every detail,

  • requires valid data and rules
  • increases complexity and computational effort
  • increases the maintenance costs
20
Q

How to build a model

Modelling topics

A 
Context
Structure
Realization
Assessment
Implementation
21
Q

How to build a model

Modelling topics

Context

A

What do I know is going on?
What do I assume?
Who is my client?
What does my client want?

22
Q

How to build a model

Modelling topics

Structure

A

What are variables and relationships?
What kinds of model should I make via what process?
How should I analyse data to understand the problem?
What are the steps in any model that define a procedure?

23
Q

How to build a model

Modelling topics

Realization

A

How can I make my model yield results?
What data is available and how will I get it?
How do I estimate parameters (that I don’t know)?
What software will I use?

24
Q

How to build a model

Modelling topics

Assessment

A

What potential value does my model offer the client?
Is my model correct, feasible, acceptable?
Will there be problems with algorithms or data?

25
Q

How to build a model

Modelling topics

Implementation

A

How can I use the model to help my client?

What does the client have to do to use the model successfully?

26
Q

How to build a model

Mistakes by novice modelers

A
  • Over-reliance on available data
  • Taking shortcuts
  • Insufficient use of abstract variables/relationships
  • ineffective self-regulation
  • overuse of brainstorming
27
Q

How to build a model

Mistakes by novice modelers

Over-reliance on available data

A
  • analysis / calculations on data

- specifying tool (Regression) rather than model

28
Q

How to build a model

Mistakes by novice modelers

Taking shortcuts

A
  • deciding on an answer
  • doing calculations
  • “magic wand”: assuming data would solve problem
29
Q

How to build a model

Mistakes by novice modelers

Insufficient use of abstract variables / relationships

A
  • tend not to define variables, want a spreadsheet

- lack of diagrams / sketches

30
Q

How to build a model

Mistakes by novice modelers

Interactive self-regulation

A

Not monitoring and evaluating progress

31
Q

How to build a model

Mistakes by novice modelers

Overuse of brainstorming

A

lack of structure

32
Q

How to build a model

Real-world problems

Characteristics and Consequences

A

Characteristics:

  • uncertainty
  • multifacetted
  • many points of view (different perceptions, perspectives)
  • several assumptions
  • messy (system of inter-related problems)
  • ambiguity and disagreement
  • always changing

Consequences:
- no single solution and no permanent solution

33
Q

How to build a model

What a model requires

A

Empirical knowledge, theoretical theses & imagination (assumptions)

  • Ideally: empirical knowledge > formal theory > imagination
  • simulations can help to explicate models, as they leave little room for hazy definitions

Exemplary modelling concepts

  • UML: Unified Modelling Language, designed for object oriented system development
  • ODD Protocoll
34
Q

Conceptual model

A

The conceptual model is a complete (software independent) description of the model, from which the computer model can be built

35
Q

Conceptual model

Components

A

Entities
- we need to know each entity

Equations

  • operationalise the relationship
  • e.g. computing time they need for shopping

Variables
- need to know all the variables that affect the entities

Relationships
- e.g. customers are friends - do they interact?

Stochasticity
- Is the equation deterministic? Are there other factors that add noise/errors?

36
Q

Conceptual modelling

A
  • always happens even if we don’t write down the conceptual model and go straight to building the model in the software (not recommended)
  • it is all the decisions about what we are going to include in the model based on our understanding of the reals system
  • needs to be related to objectives of the project
  • often considered as an “art” and learnt with experience
37
Q

Conceptual models

Seek the model that yields the best project performance

A

Results
- scope of model output, accuracy, understanding

Future use
- model portability

Confidence in the model
- Verification, validation, credibility

Resources
- build time, run time, time to analyse results, hardware requirements

38
Q

Conceptual models

Simple models

Advantages and disadvantages

A

Advantages:

  • easier to understand
  • quicker to build, test, run, change
  • need less data
  • easier to experiment with
  • easier sensitivity analysis

Disadvantages:

  • may have less validity
  • > may be less accurate (may be further away from the real world) (although not necessarily)
  • > perhaps less confidence in the model
  • > Hence we want the simplest valid model
  • > simple: make assumptions about the system
39
Q

Conceptual models

Assumptions

When are assumptions made?

A

Assumptions are made when there is a lack of knowledge about the real system

40
Q

Conceptual model

Assumptions

What should assumptions be assessed for?

A

Confidence:

  • the level of certainty that the assumption about the way the real system will operate is correct
  • > approximately large enough

Impact:

  • The estimated effect on the model results if the assumption is incorrect
  • > What are the implications if the assumption is incorrect?
41
Q

Conceptual model

Assumptions

Options when dealing with low confidence, high impact assumptions

A
  • worst case
  • sensitivity analysis
  • decision variable
  • more information, e.g. measure information about customer arrival
  • highlight in results
  • stop project -> if the assumption is very high impact, that if we are wrong it would turn over our results, we may have to stop the project
42
Q

Conceptual model

Simplifications

What are they and what should they be assessed for?

A

Simplifications are choices to model the system in a simpler way. They are the essence of modeling.

Should be assessed for impact:
- the estimated effect on the model results of making the simplification

43
Q

Conceptual model

Simplifications

What types of simplifications are there?

A
  • leave things out (model scope)
  • group activities and entities
  • restrict range of values of variable
  • use different type of model (aggregation, simplification)
  • scale - slow changes as constants, averages (beware!) (Are we looking at one day or weeks?)
44
Q

Conceptual model

Simplifications

Options for medium or high impact simplifications

A
  • remove simplification (model the factor) (Is it worth to simplify)
  • take account of simplification in result analysis
45
Q

Conceptual model

Simplifications

Simplifying an existing model

Advantages and disadvantages

A

Any model is a simplification. Simplifying an initial model may be a good approach

Advantages:

  • increased understanding
  • easier experimentation and analysis of results
  • cross validation and verification

Disadvantages:

  • time consuming
  • danger of misleading results (removing entities from the model can lead to misleading results)
  • reduced experimental frame

Advanced Business Analytics (11 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions