First Planimate® Model - the ‘Perils of Average Thinking’: Difference between revisions

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Press a few buttons, run it etc, look aroundā€¦ Ā 
Press a few buttons, run it etc, look aroundā€¦ Ā 


<br> Start a New Model (Discard current) Ā 
<br> Start a New Model (Discard current) Ā 


If not already visible, show the Object Palette / sidebar (View / Object Palette). Ā 
If not already visible, show the Tools Menu / sidebar (View / Tools Window). Ā 


You can drag objects off the palette or click them and then click where you want them. Ā 
You can drag objects off the menu tree (left mouse click on object and hold while dragging) onto the panel. Ā 


Drag them about, Ā 
Drag them about, Ā 
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Copy and Paste them. Ā 
Copy and Paste them. Ā 


Save, and then start a New model. Ā 
Save, and then start a New model. Ā 


Now for <u>your</u> first PlanimateĀ® model - a Distribution Centre. Ā 
Ā 
Now for your first PlanimateĀ® model - a Distribution Centre. Ā 


Change to Object View &lt;Ctrl+O&gt;. Ā 
Change to Object View &lt;Ctrl+O&gt;. Ā 


Make the Clock visible using View / Simulation Clock. Ā 
Make the Clock visible using View / Simulation Clock. Ā 
[[Image:sim clock.png]]


Your mouse pointer should look like: [[Image:Object edit mouse pointer.png]] Ā 
Your mouse pointer should look like: [[Image:Object edit mouse pointer.png]] Ā 
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Reflect on the question: ā€œ<u>Where</u> is the Distribution Centre?ā€. Next we need to define the items that will move through and interact with these objects in the Distribution Centre. Ā 
Reflect on the question: ā€œ<u>Where</u> is the Distribution Centre?ā€. Next we need to define the items that will move through and interact with these objects in the Distribution Centre. Ā 


Go to Menu Bar / Edit / Item Classes / Add New Item Class. Ā 
Go to Menu Bar / Edit / Item Classes / Add New Item Class. Ā 
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To make things more interesting, right click the Entry and select Mode. Select ā€œPeriodic Arrivalsā€ from the list. OK and run the model again. Ā 
To make things more interesting, right click the Entry and select Mode. Select ā€œPeriodic Arrivalsā€ from the list. OK and run the model again. Ā 


<br> Ā 
<br>


== ObservationsĀ  ==
== ObservationsĀ  ==

Revision as of 12:13, 1 March 2016

Explanation

This model will introduce you to basic elements of the PlanimateĀ® platform and also provide some initial experience with some important general concepts concerning the dynamic behaviour of systems.


We are going to take a look at a simple system and its dynamics.

The major dynamic events of this system will be as follows:

Arrivals = Entry into System.
Waiting Around = Queuing for Service.
Processing = by a Server.
Departure = Exit from System.


The thing to appreciate here is:

  • We want to make a model of a system to see, learn or show others how it works.
  • In this system, events occur and it changes over time.
  • Hence the model must be dynamic.


Imagine we could isolate an area in a real system (e.g. a set of office cubicles), then watch and record what happens in it.

A video recording is one possible strategy for recording a systemā€™s activities. To fully cover the system area, you may need many simultaneous recording streams. And what would you record - the people or the paper?

You would then also need to set up an array of screens for the viewer to watch the playbacks.Too many screens and not enough eyes makes forming a useful understanding of cause and effect in this system very elusive. And even then, there is still a need to be able to communicate this behaviour to others, so that they can understand it, before you can then fruitfully discuss future options for the system.

To be able to give this information to somebody else to review, some kind of language is needed to represent how the events going on in that system relate to each other, things like:

  • what causes what,
  • what waits for what else,
  • what becomes what else,
  • what decisions are made,

ā€¦ and much more.

With a standard language for this, we can use a machine (the PC) to build this system model.

This is highly useful, because in this kind of machine we can use the language to reconfigure our system model to represent a variety of current interpretations of, or future options for the system being examined. These representations can be stored, transmitted, and reproduced in other machines, in other locations, before other eyes.

PlanimateĀ® is a language for specifying the elements and behaviours of a dynamic system.

To represent a system in a Planimate model, we first need to learn about its building blocks and basic concepts.


Hands On

Launch Planimate and explore it a little.

Maximize the window.

Load a simple model from the Intro Folder

Press a few buttons, run it etc, look aroundā€¦



Start a New Model (Discard current)

If not already visible, show the Tools Menu / sidebar (View / Tools Window).

You can drag objects off the menu tree (left mouse click on object and hold while dragging) onto the panel.

Drag them about,

Rename them by clicking on the names.

Right-click on them to produce their editing menu.

Delete them.

Copy and Paste them.


Save, and then start a New model.


Now for your first PlanimateĀ® model - a Distribution Centre.

Change to Object View <Ctrl+O>.

Make the Clock visible using View / Simulation Clock.

Sim clock.png

Your mouse pointer should look like: Object edit mouse pointer.png

From the object palette, Add an:

Entry Items cross the system boundary and enter a system from an Entry.

(NB: an Entry has no Capacity)

Queue Items wait in Queues to access other objects.
Multiserver We use the default Single Capacity (only holds one thing).

Rename it to ā€œLoad or Unloadā€.

Exit. Items leave the system through the exit, it is another boundary.

(NB: an Exit has no Capacity)


Line them up from left to right. These are the objects of our system.

Save your Model (We will call this Model DC01).

Reflect on the question: ā€œWhere is the Distribution Centre?ā€. Next we need to define the items that will move through and interact with these objects in the Distribution Centre.


Go to Menu Bar / Edit / Item Classes / Add New Item Class.

Give it the name ā€œVehicleā€

Notice the following things:

  • An Item icon appears in the Item Palette.
  • The mouse pointer changes to show: Flow edit mouse pointer.png
  • (Flow) indicator in PlanimateĀ®ā€™s Window Title.

Now create a path from Entry > Queue > Server > Exit by clicking on each in turn, from left to right.

Path simple.png

Type Ctrl-O to get back to object mode and save the model.

Run the Model. One item will move across the model. OK the dialog that announces the model has finished.

To make things more interesting, right click the Entry and select Mode. Select ā€œPeriodic Arrivalsā€ from the list. OK and run the model again.


Observations

Now it is time to study the model and make and discuss observations, so you get a better understanding of the language being used in a PlanimateĀ® dynamic model.

Here are some questions for you to examine:

  • Can you explain what the clock is doing?
  • Can you identify the different ā€œEventsā€ in this simulation?
  • Does the clock ā€œTickā€ during Animation?
  • How many items animate at a time?
  • Does the animation of an Item from one object to the next involve the passing of time - if so, which time?
  • What is the Inter-Arrival Time between items?
  • What is the processing delay time at the Load or Unload server?
  • Is there any queuing - are any items waiting to enter the load or unload process?
(look VERY carefully here).
  • How does the processing capacity of this system compare to the demand placed on it?
  • What is the efficiency of this system?


Is this the ā€œreal worldā€?

  • Are ā€œcustomerā€ arrivals always normally steady?
  • Do all processes always take exactly the same time?
  • Do we expect to always get serviced immediately?


We need to make this model more realistic.


More work is needed on INTERACTIONS between Items and Objects.

There are a number of types of interactions occurring in this model already:

  • Item Arrivals at the System boundary - INTER-ARRIVAL TIME.
  • Items passing through the fixed entities - PROCESSING TIME.


These interaction times can be made to vary from one item to the next, to make the system more realistic.

Letā€™s set up some parameters to go into these interactions:

Time Parameter Average Variation Pattern Deviation
Inter-Arrival 5min 0sec Equally Likely

(aka Uniform Distribution)

2min 48sec
Load or Unload 5min 0sec Bell Curve

(aka Normal Distribution)

1min 6sec

Regardless of the variation pattern, we will keep the averages the same.


More Hands On

Go to Menu Bar / Edit / Interactions (Ctrl-I is a shortcut).

Notice the following things:

  • The mouse pointer changes to show:
  • (Interact) indicator in PlanimateĀ®ā€™s Window Title.
  • The Item Path appears in RED.


Click on the ENTRY

  • Click the Pattern button next to Interval
  • Configure the Distribution Pattern Dialog as above
(Equally Likely / Mean 0:5 / Range 0:2.48).
  • Click on the Preview Plot button to View the distribution pattern.
Training entry equally likely dist.png
  • Click OK to close both dialogs.

Right click on the Load or Unload Server Object

  • Select Bell Curve / Mean 0:5 / StdDev 0:1 06 in the Distribution Pattern Dialog.
  • Click Preview Plot to View distribution pattern.
Training entry normal dist.png

Notice the range of possible values in the sample plot. While the graph may show numbers less then zero the minimum will be clamped to 0 as time cannot go backwards.

Save, Run and Observe the Model

  • Notice any new behaviour.
  • Queuing now happens.
  • Let us trace the queuing activity.

Go back to object mode (Ctrl-O) and right click on the Queue.

Select ā€œLog Attributes To Fileā€.

Tick the Queue 1 Occupancy row and OK the dialog.

Save and Run the model. Now click and pause the model (or press ESC).

We will now make use of the time acceleration features of PlanimateĀ®.

With the model paused, right click on the background of the model. The Advance to Time & Advance for Interval options enable you to ā€˜fast-forwardā€™ the model up to a future point of time.

Select Advance For Interval in the background menu and enter ā€œ20dā€ in the dialog. This will advance for 20 days. After the advance (it will be quick) select ā€œPauseā€.


Viewing Results Graphically

Planimate models often contain graphs and views of data, but for simplicity in this tutorial we will use a pre-made Planimate application to study our model's performance.

The ā€œLog Attributes To Fileā€ option we selected earlier has caused the Queue's occupancy to be written to a log file.

Select menubar / Tools / Log Viewer. This will open another window, the Planimate Log Viewer application.

From its menu bar select File / Load Data. Browse to the folder where you saved the Distribution Centre model, In this folder you should find dc01_01. Upon opening this file you will receive a ā€œLog Data Successfully Loadedā€ message. OK It.

The viewer will switch to a new page and you will see Queue 1 Occupancy on the left. Click the cross in the left hand column, it will change to a tick and a graph will appear.

Training first q1 log.png

Notice the peak of the instantaneous graph is at 10. Lets examine this closer. Alt-tab back to Planimate and run model again. Use the Advance To Time again but this time select ā€œContinueā€ instead of pause. Look closely at the Queue and Entry.

The default Queue has a capacity of 10 items, 5 visible and 5 indicated by the counter. You can see that the queue is full. This means the next item out of the Entry has nowhere to go and the Entry indicates this ā€œblockedā€ situation by becoming red.

Training blocked entry.png

Blocking an Entry is bad for a simulation because it means items that were scheduled to arrive are not making it into the simulation.

RULE: Never Block an Entry because it will distort demand on your system.

Letā€™s try ā€œfixā€ the system by adding a bigger queue capacity.


Exploratory Exercise

Lets make a bigger queue capacity (100).

Stop the run and (in Object Mode) right click on the Queue and set its Capacity to 100.

Run the model again, advance for 20 days then stop the run.

Alt-tab back to the Log Viewer and load the dc01_01 file again.

Training second q1 log.png

The maximum occupancy of 40 is within the queue's limit... but even averaged, there are over 25 waiting in the queue for a process which on average takes 5 minutes each!

Try run the model for 100 days and look at the occupancy.

Training third q1 log.png

This is an unstable system.

Even though the average rates of arrival and service match, due to the accumulating effect of the variation, the longer system runs, the longer the queue (and the wait) will get.


Reflect and Discuss

What is going on hereā€¦

  • The system experiences serious, significant instability, evidenced by rapid growth and shrinkage in queue occupancy. Will you have room for a large enough queue?
  • What will customers and managers think of the waiting times?
  • Where does catch up come from, and how does it happen?
  • Are there any useful control mechanisms is the current system?.


Discuss the causes of the instability of this system.

  • Dependency due to the Queue?
  • Variation in arrivals, and/or in the server?
  • The queue size limit?

Actually the thing you can really blame is full utilisation... What..?


If you had until now assumed that::

A ā€˜balanced systemā€,
where average process capacity = average demand,
would also be a ā€˜stableā€™ systemā€¦

has this experience altered that view?


What will it take to ā€œtameā€ this system?

  • We need to design Recovery Time into it.
  • How do we go about this?


Utilisation Ratio Exercise

Lets say we found a way to reduce our loading time by just 5%. How would it affect the chaotic behaviour of the model?

With the DC01 model loaded, type Ctrl-I to edit interactions and click on the Multiserver (Load or Unload). In the distribution dialog, set the scaling field to 0.95 instead of 1.0

This change will scale back the process time for all items so they will (on average) be 5 percent shorter. Effectively, we have added 5 percent extra capacity to the server in this subsystem.

Run the model and investigate the logged results.


Reflect and Discuss

Thereā€™s nothing like a bit of extra capacity to tame an unstable system!

Here is the shape of the general relationship between utilisation and average queue length:

Training utilvsQlength.png

  • now the question to be addressed will be.. how much capacity to supply?

Change the scaling to test different what-ifs. You will get a good idea of how the instability relates to utilisation ratio.

You can actually do a wide range of explorations with this issue, because the nature of the curve is affected by both the pattern and degree of variation, and the interplay between the arrival and processing patterns.

Knowing the effects of the utilisation ratio, the question of how much capacity to supply is now not only solvable, but justifiable to other decision makers who may also carry the assumption about full utilisation being a worthwhile pursuit.

Knowing what circumstances you donā€™t want to happen (full utilisation), you can now take on the possibility of managing customer expectations. The issue to focus on now is customer experience, rather than ā€œfire-fightingā€.

How much stability and recovery time do you want or need, to meet customer expectations?

The way is now open for you to study the impact of fostering various customer behaviour, as well as the provision of supply capacity.

Both supply AND demand dynamics can be studied, and understood.

The next step will be to take this new appreciation of the non-linearity of system behaviour forward as we use the language of PlanimateĀ® to refine the representation of a Distribution Centre that we have achieved so far.

Next: Evolving your first model.

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