9 - Simulation Modelling

Question 1

What is a simulation and why do we need it?

Simulation

Answer 1

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

Question 2

What is a simulation and why do we need it?

Simulation vs. real world

Answer 2

• 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

Question 3

What is a simulation and why do we need it?

Simulation vs. real world

The paradigms

Answer 3

• discrete-event-based
• agent based (how do we look at decision making in the system?)
• system dynamics (continuous vs. discrete changes)

Question 4

What is a simulation and why do we need it?

Motivation: Simulation …

Answer 4

• 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)

Question 5

What is a simulation and why do we need it?

When not to use a simulation?

Answer 5

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

Question 6

Discrete-event simulation

Answer 6

• 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

Question 7

Discrete-event simulation

Some concepts of discrete event simulation

Entity

Answer 7

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

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

Question 8

Discrete-event simulation

Some concepts of discrete event simulation

Entity Queue

Answer 8

• 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)

Question 9

Discrete-event simulation

Some concepts of discrete event simulation

Events

Answer 9

• 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

Question 10

Discrete-event simulation

Some concepts of discrete event simulation

Event list

Answer 10

List of all events with their respective time of occurrence

Question 11

Discrete-event simulation

Some concepts of discrete event simulation

Simulation clock

Answer 11

Variable Stating the current time in the simulation model

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

Question 12

Discrete-event simulation

Some concepts of discrete event simulation

Statistics indicator

Answer 12

• Variable storing statistic information about the system

- Example: Mean turn-over rate

Question 13

Discrete-event simulation

Some concepts of discrete event simulation

Entity state

Answer 13

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

Question 14

Agent-based simulations

What is an agent-based simulation?

Answer 14

• 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

Question 15

Agent-based simulations

What are agents?

Answer 15

• 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

Question 16

System-dynamic simulations

Answer 16

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

Question 17

How to build a model

Conflict

Answer 17

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

Question 18

How to build a model

Ways to derive a model from the real system

Answer 18

Reduction:
- abandonment of unimportant system components

Abstraction:
- generalization of specific system characteristics

Question 19

How to build a model

Solution to the conflict

Answer 19

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

Question 20

How to build a model

Modelling topics

Answer 20

Context
Structure
Realization
Assessment
Implementation

Question 21

How to build a model

Modelling topics

Context

Answer 21

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

Question 22

How to build a model

Modelling topics

Structure

Answer 22

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?

Question 23

How to build a model

Modelling topics

Realization

Answer 23

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?

Question 24

How to build a model

Modelling topics

Assessment

Answer 24

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

Question 25

How to build a model Modelling topics Implementation

Answer 25

How can I use the model to help my client? | What does the client have to do to use the model successfully?

Question 26

How to build a model Mistakes by novice modelers

Answer 26

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

Question 27

How to build a model Mistakes by novice modelers Over-reliance on available data

Answer 27

- analysis / calculations on data | - specifying tool (Regression) rather than model

Question 28

How to build a model Mistakes by novice modelers Taking shortcuts

Answer 28

- deciding on an answer - doing calculations - "magic wand": assuming data would solve problem

Question 29

How to build a model Mistakes by novice modelers Insufficient use of abstract variables / relationships

Answer 29

- tend not to define variables, want a spreadsheet | - lack of diagrams / sketches

Question 30

How to build a model Mistakes by novice modelers Interactive self-regulation

Answer 30

Not monitoring and evaluating progress

Question 31

How to build a model Mistakes by novice modelers Overuse of brainstorming

Answer 31

lack of structure

Question 32

How to build a model Real-world problems Characteristics and Consequences

Answer 32

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

Question 33

How to build a model What a model requires

Answer 33

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

Question 34

Conceptual model

Answer 34

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

Question 35

Conceptual model Components

Answer 35

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?

Question 36

Conceptual modelling

Answer 36

- 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

Question 37

Conceptual models Seek the model that yields the best project performance

Answer 37

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

Question 38

Conceptual models Simple models Advantages and disadvantages

Answer 38

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

Question 39

Conceptual models Assumptions When are assumptions made?

Answer 39

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

Question 40

Conceptual model Assumptions What should assumptions be assessed for?

Answer 40

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?

Question 41

Conceptual model Assumptions Options when dealing with low confidence, high impact assumptions

Answer 41

- 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

Question 42

Conceptual model Simplifications What are they and what should they be assessed for?

Answer 42

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

Question 43

Conceptual model Simplifications What types of simplifications are there?

Answer 43

- 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?)

Question 44

Conceptual model Simplifications Options for medium or high impact simplifications

Answer 44

- remove simplification (model the factor) (Is it worth to simplify) - take account of simplification in result analysis

Question 45

Conceptual model Simplifications Simplifying an existing model Advantages and disadvantages

Answer 45

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