Lecture 7: DES Flashcards

1
Q

What are the two types of Systems?

A
  1. Discrete
    - system where status variable change instantenously at discrete intervals of time
    - > IT systems, manufacturing system, traffic regulation systems
  2. Continous
    - status variables change continously during time of simulation
    - > flow of fluids heat
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2
Q

What is the difference between analytical and experimental models?

A

simple systems can u can use mathemtical models (algebral, calculus, or linked to probability)

→ if its too complex (to many variables having interdependancies) u need experemental models throught simulation

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3
Q

Explani deterministic and stochatic models

A
  1. Deterministic
    - no uncertainty in simulated phenomena (e.g. customers arriving at regular times)
  2. Stochastics
    - correlation between variables and probabilities
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4
Q

Types of Simulations

A
  1. Statis vs Dynamic
    → does time play a role? (do some parameters change in generell)
  2. Continous vs Discrete
    → do changes take place continously or instantenously at certain points
  3. Deterministic vs Stochastic
    -is there randomness in the inputs or operations of the model (how likely is it for a mashine to go out?)
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5
Q

What is SImulation

A

→ Simulation is the imitation of the operation of a real-world process or system over time.

→ Computer simulation is a methode where experiments are conducted on a computer in order to tecreate over time the functioning of a system

experimental tequnique allows to predict the behaviour of a system under certain conditions that most likely will manifest while the system

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6
Q

Whats the function of Simulation?

A

→ recreate behaviour of a system over an extended period of time, in a shorter time

input X → model → output Y

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7
Q

How does Simulation supports in the industrial sector?

A
  • Desgin of new systems/solution (e.g. factory)
  • improvment of actual systems/solutions
  • Performance analysis (e.g. productivity, bottlenecks, resources saturation)
  • WHAT-IF analysis (new order from customer into system)
  • Training
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8
Q

What are Discrete event systems? + Examples

A

→ dynamic systems (over time) which update their values only at discrete number of points

  • no continously changing states are of importance

Examples: call center, gas station, restaurant factory, warehouse
(restaurant: customers enter at certain time, they sit at certain etc.)

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9
Q

What is the Approach of DES

A
  • focus on times between events
  • with full knowledge we can update the system only at this specific time point
  • full info about history, cause nothing happened between t1 and t2
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10
Q

What is an Entity of DES?

A

-thing with distinct and temporary existence that passes through a system (e.g a piece that should be assemblet, client to the bank office)

→ created to simulate a process and then destroyed

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11
Q

What is a Resource of DES?

A

thing with active and permanent existence that provides a service to entities (e.g. machine tool, conveyor, a banker, …)

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12
Q

What is a queue of DES?

A

a thing with temporary and passive existence that is formed by entities which are waiting for a resource (that is not free at that time).

→ always there, but can be empty or full, so temporary

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13
Q

What are Variable and Attributes of DES?

A

data and information about entities or resources (e.g. warehouse capacity, cycle time).

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14
Q

What is the Status of DES?

A

Description of all the values attributes of an entity or a resource, in a specific instant of time.

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15
Q

What is an event in DES?

A

Instant of time in which the model status is changing (change of status of entity or resource).

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16
Q

What are Activities in DES?

A

Operations done between two events (started and ended)

→ has a start and end time, so time between events

17
Q

What are the Simulation Phases?

A
  1. Real System (Problem)
  2. Conceptual Model
  3. Computational model
  4. Solution
18
Q

What is the first Phase of the simulation about?

A

→ describe the Real Sysmte + Problem definition

-identifiy objectives and constraints (what do you want to simulate?)

  • define PJ plan:
    1. choose the differen simulations sceanario
    2. PJ time (how long simulation takes)
    3. resources, team, different roles (customers has to give data)

Example:

  • want to measure the production time
  • constraint: 5 plants available for 8hours a day
19
Q

What is the second phase of the simulation process?

A

→ Conceptual Model - detail and accuracy of the model

  1. Variables (e.g. production plant design or size)
    - exogenous (external cause, process time)
    - decisional (machines numbers)
    - endogenous (internal cause, e.g. saturation rate)
  2. Constraints
  3. Performance Measurements
    - endogneous variables
    - choose the objective functions

→ the more detailed, the more accuracy is reached. BUT with too many details the accuracy shrinks

20
Q

What is the importance of Data colleting in simulating?

A

-sometimes quite hardworking (digitalization solved it)

-Data quality influences model quality (GIGO effect = garbage in, garbage out)
→ bad data, bad result

  • if data is missing, u should adjust the model to keep accuracy high
  • not enought data lead to statistical distributions

-

21
Q

What Simulation Software can u use?

A

Generel-Purpose Languages

  • procedural (Fortran, C, Pascal, Basic)
  • object-oriented (C++, Objective C, Java)

Simulation Languages
-Siman, Slam, Modism

Simluatios
-simulation packages (e.g. PlantSimulation, Arena, Simul8, Matlab-Simulink)

22
Q

What are some criteria are there for software?

A
  • Flexibility

– Ease of development

– Model execution speed

– Animation

– Output visualization

– Statistics

– Interface with other data

– Reusability

– Customer support

– Cost

→ has to be able to instantiate the different parts of the sysmem (enitiies, resoucres, queues, ACs) and describe different behavoiur of system (status, events, ACs)

23
Q

Was ist Model Assessment and Validation?

A

→ Assessment: accuracy, completeness and consistency of the simulation model respect to conceptual model (Did you do the things RIGHT?)
→ no bugs or problems in the code

→ Validation: accuracy, completeness and cosistency of the simulation model respect to real system
(Did you do the RIGHT things?)

→ solution fits the charateristics of the real system. Is performance in the model = performance real world)

24
Q

What is the digital twin paradigm?

A

→ the simulation model is a Virtual replica synchronized with the field

  • Digital Model → is the simulation model core (just manual data flow)
  • Digital shadow → is the simulaion model that receives data and info from the system (automatic flow from physical to digital object)
  • Digital Twin → is a digital shadow that can also feedback the decision and optimizations on the field (automatic data flow)