Simulace.info - User contributions [en]

WS 2015/2016

2016-01-24T22:17:43Z

Knam00: /* Simulations */

Semestral papers from winter term 2015/2016. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2015/2016|assignment approved]].

==Simulations==
--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 15:35, 15 January 2016 (CET) [[Maze Solving Robot Simulation]]

--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 22:30, 15 January 2016 (CET) [[Aircraft boarding methods]]

--[[User:OtakarTrunda|OtakarTrunda]] ([[User talk:OtakarTrunda|talk]]) 11:48, 16 January 2016 (CET) [[Ultimatum Game]]

--[[User:Xpokl18|Xpokl18]] ([[User talk:Xpokl18|talk]]) 06:51, 17 January 2016 (CET) [[RetrieverBreeder]]

--[[User:Martin.zima|Martin.zima]] ([[User talk:Martin.zima|talk]]) 10:28, 17 January 2016 (CET) [[Lane-merge optimization]]

--[[User:Xhudj17|Xhudj17]] ([[User talk:Xhudj17|talk]]) 11:25, 17 January 2016 (CET) [[Crossroad simulation]]

--[[User:xtomp36|xtomp36]] ([[User talk:xtomp36|talk]]) 15:33, 17 January 2016 (CET) [[Load-balancing]]

--[[User:knam00|knam00]] ([[User talk:knam00|talk]]) 23:16, 24 January 2016 (CET) [[Heating System Simulation]]

==Papers==
--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 18:14, 22 January 2016 (CET) [[System Archetypes]]

--[[User:Xpokl18|Xpokl18]] ([[User talk:Xpokl18|talk]]) 07:44, 23 January 2016 (CET) [[PPC systems, Ad Rank]]

--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 14:47, 23 January 2016 (CET) [[System Dynamics]]

--[[User:Knam00|Knam00]] ([[User talk:Knam00|talk]]) 17:11, 24 January 2016 (CET) [[Shifting the Burden Archetype]]

--[[User:xtomp36|xtomp36]] ([[User talk:xtomp36|talk]]) 22:29, 24 January 2016 (CET) [[Customer segmentation techniques]]

User:Knam00

2016-01-24T22:15:46Z

Knam00: Created page with "Martin Knapovský: Heating System Simulation"

Martin Knapovský: [[Heating System Simulation]]

Heating System Simulation

2016-01-24T22:15:08Z

Knam00: Martin Knapovský - knam00 - Heating system simulation - System dynamics

*'''Project name''': Heating System Simulation
*'''Class''': 4IT496 (WS 2015/2016)
*'''Author''': Bc. Martin Knapovský
*'''Model type''': System dynamics
*'''Software used''': Vensim PLE - Windows version

----

=Introduction=
Most households in the Czech Republic have mains gas central heating. This is a so-called ‘wet system’, which means a gas-fired boiler heats water to provide central heating through radiators and hot water through the taps in your home. Some houses that aren’t connected to the gas network can use electrical heating or liquid petroleum gas (LPG) or heating oil, which work in a similar way to gas central heating, although LPG and oil are delivered by road and stored in a tank, which you may have to buy or rent from your supplier. Gas is a highly efficient fuel, so you get a good return on every unit of energy. Modern condensing boilers, which use hot flue gases that are wasted in a standard boiler, have very high efficiency. Some are now 90% or more efficient.

I have recently acquired condensing boiler and i wanted make a simulation of heating system for measuring approximate amount of gas burned during 4 months. This period has been shortened to 1 month, because there is no external variable that would change outside temperature and therefore system would behave in a same manner. There has also been problem with excessive amount of data (more information under model description). I also wanted to know how to set our thermostat, so that it is not so cold in private rooms.

=Problem definition=
[[File:Heating simulation - Apartment Layout.png|thumb|frame|center|Apartment layout]]

==People==
There are 6 people using this flat. Each person periodically comes and leaves.
*Pair 1 comes on monday and leaves on Wednesday
*Pair 2 comes on monday and leaves on Friday
*There are also 2 boys sharing another room - The first one stays here for the whole week and the second one leaves on Saturday and comes on Monday.

==Gas boiler==
We own 25kW condensing boiler with 95% efficiency.
Boiler is controlled by thermostat.

==Thermostat==
Thermostat is placed in the living room - it is set to 22 degrees Celsius from 7:00 to 23:00 and to 19 degrees Celsius from 23:00 to 7:00.

==Rooms==
There are 3 separated rooms used privately, one living room, kitchen, bathroom, storage room and toilet.
He have high ceilings, so volume of rooms is really big.

*Private rooms - 84mˆ3 x 3
*Living room - 168mˆ3
*Kitchen - 84mˆ3
*Bathroom - 21mˆ3
*Storage room - 21mˆ3
*Toilet - 21mˆ3

Each room has different coefficient of ventilation - windows are different, placement is different - for example there are no windows in living room and there is window in bathroom that is often opened, because someone just took shower.

==Radiators==
We have old radiators made from cast-iron. Each part of this radiator is 600mm high and 200mm deep, its surface is 0,31mˆ2, its capacity is 1,7L.
Each room (except storage room) has its own radiator.
In total, there are 5 radiators with 30 parts and 2 radiators with 15 parts.

==Heating system==
Consists of radiators and tubes. Total amount of water inside heating system is 255L (5 big radiators) + 51L (2 smaller radiators) + 61L (Amount of water inside tubes - 108m of tubes in total) = 367L.
There is also need to define settings of heating system. Our heating system is designed in a way that water is heated up to 50 degrees celsius - this is the point where gas boiler stops and energy accumulated within water is distributed.

=Method=
I have chosen Vensim for modelling '''system dynamics'''. I must say, that this software is subjectively user-unfriendly - changing the model is quite difficult due to necessity of usage of all variables defined in model - there can't be any variable that is not used. The same goes for usage of those variables inside equations - if there is a link, you have to use your variable inside equation - Vensim requires clean model - that is good for final release, but not for development. Versions for windows and mac (that i own) behaves in a different way. On Mac OS X, there is problem in editing properties of variables and links - software is buggy (version 6.3 PLE). I have solved this problem by creating virtual machine and installing Windows XP. On this system, everything worked fine. (Notes for potential students using Vensim)

=Model=
In this description, I will go through model settings, model overview and then I will describe model segments.

==Global model settings==
Due to need of some universal time step for implementing physical laws into model, I have chosen '''1s step'''. One second is basic unit of SI system, therefore physical equations and derived physical units are using this time-length.
Model starts at time-step 0 and end at time-step 2592000, which means that this abstract system dynamic model runs for 1 month (30 days) or real time.

==Model overview==
[[File:HSS - global.png|upright|frame|center|Model overview]]

===Model parameters===
As you can see, model is quite complex - it is mainly due to model versatility - it is possible to set various model properties:
*'''Gas boiler power''' - default power is 25000J (25kW)
*'''Gas boiler efficiency''' - default efficiency is 95%
*'''Water heat capacity''' - in real environment, water heat capacity slightly changes according to temperature. In this model, water heat capacity stays the same all the time - it can be changed manually - default value is 4181J.kgˆ(-1).Kˆ(-1) - it means, that 1kg of water needs 4181 Joules to increase its temperature by 1 Kelvin.
*'''Total tube length''' - length of tubes in our apartment - default value is 108m
*'''Tube water per meter''' - default value is 0.58L
*'''Heat curve''' - difference between water temperature and radiator temperature - this variable is set according to heating system design - its default value is 0.8 (circulating water has 50 degrees C. -> radiator has 40 degrees)
*'''Room1S - Room7S''' - room sizes - Room1 - Room4 has 83mˆ(3), Room5 and Room6 has 21mˆ3, Room7 (living room with thermostat) has 168mˆ3
*'''R1S - R7S''' - radiator sizes - R1 - R5 are radiators with 30 parts, R6 and R7 are radiators with 15 parts
*'''Radiator surface per part''' - this variable is needed to compute radiated energy - default value is 0.31mˆ2
*'''Air density''' - default value is 1.2 kg.mˆ(-3)
*'''Air heat capacity''' - default value is 1000J.kgˆ(-1).Kˆ(-1)
*'''Outside temperature and Base temperature''' - default value 10 degrees Celsius
*'''Ventilation coefficient''' - used for computing heat decrease inside rooms (each room has different multiplier)

===Global system dynamics===
Thermostat switches gas boiler on and off according to temperature in living room and water temperature. According to the people in the flat, radiators are turned on or off and amount of circulating water changes - this affects effectivity of heating (more water, slower temperature increase). Gas boiler heats-up water and this water is distributed through the system. With respect to Stefan-Boltzman law, heat is radiated into room and air temperature inside room changes. Accumulated heat stays in heating system and inside rooms and slowly degrades. Thermostat stops heating system when temperature requirements are satisfied. The system doesn't count with using hot water for shower.

== From boiler to water ==
[[File:HSS-Thermostat-to-water.png|frame|center|Boiler to water simulation part]]
===Thermostat===
Thermostat is placed inside the living room - it is set to 22 degrees Celsius from 7:00 to 23:00 and to 19 degrees Celsius from 23:00 to 7:00. These settings are triggered by variable called '''StopTemperature''' with this setting <br/>
<code>IF THEN ELSE(MODULO(Time, 86400) > 25200 :AND: MODULO(Time, 86400) < 82800, 22, 19)</code>
86400 is time in seconds - one day, 25200 corresponds to 7:00 and 82400 corresponds to 23:00.

Thermostat activates gas boiler only when there is low temperature in living room and when circulating water temperature is below 50 degrees Celsius. <br/>
<code>IF THEN ELSE(Room7Temp < StopTemperature :AND: TemperatureOfWater < 50, 1, 0)</code>

===Pairs===
Pair2 and similarly Pair1 generates discrete function with periodicity of 1 week (604800 seconds). <br/>
<code>IF THEN ELSE(MODULO(Time, 604800) >= 0 :AND: MODULO(Time, 604800) <= 432000, 1, 0)</code>

===Gas boiler===
Gas boiler runs only when thermostat generates high state. <br/>
<code>IF THEN ELSE( Thermostat = 1 , GasBoilerPower, 0)</code>

===Amount of circulating water===
Amount of water changes according to Pair1 and Pair2 state. Boys doesn't affect system, because boy 1 is always present - radiators R3-R7 are always active.<br/>
<code>
(IF THEN ELSE(Pair1 = 1, R1S*RadiatorWaterPerPart, 0)) +
(IF THEN ELSE(Pair2 = 1, R2S*RadiatorWaterPerPart, 0)) +
R3S*RadiatorWaterPerPart +
R4S*RadiatorWaterPerPart +
R5S*RadiatorWaterPerPart +
R6S*RadiatorWaterPerPart +
R7S*RadiatorWaterPerPart +
TotalTubeWater
</code>

===Gas inflow rate===
Inflow rate is increment of burnt gas per 1s. We have 25kW gas boiler with 95% efficiency.<br/>
<code>GasBoiler*(1/GasBoilerEfficiency)</code>

===Amount of gas burned===
Total amount of gas burned is integration of gas inflow rate over time. It is in Joules. Initial value is 0J.<br/>
<code>INTEG(GasInflowRate), E0=0</code>

===Water heat-up rate===
Heat up rate is increment of water temperature per 1s in degrees Celsius. If there is more water in the system, then increment is lower and vice versa. Gas boiler gives energy in Joules, that is accumulated in current amount of water. 1kg of water needs 4181 Joules to increase its temperature by 1K (correlation of K and C is linear - we can say that 1K increase is the same as 1C increase). <br/>
<code>(GasBoiler / (WaterHeatCapacity * AmountOfWaterInCurrentSystem))</code>

===Water cooling-down rate===
Water naturally loses its accumulated energy - environment is colder - temperatures are being equalized, water capacity is roughly 10 times higher.<br/>
<code>((TemperatureOfWater-BaseTemperature)*Ventilation)/10</code>

===Temperature of water===
Water accumulates temperature increments. Temperature starts at BaseTemperature (10 degrees Celsius by default).<br/>
<code>INTEG((WaterHeatingUpRate-WaterCoolingDownRate)), T0 = BaseTemperature</code>

==From water to air==
[[File:HSS-Water-to-air.png|frame|center|Water to air simulation part]]

===Radiators===
There are 2 radiators, that can be closed a 5 radiators that are always opened. Temperature of radiator is correlated to temperature of circulating water. Iron heat capacity is lower than water heat capacity (400J.kgˆ(-1).Kˆ(-1) vs 4168J.kgˆ(-1).Kˆ(-1)), thus radiator itself cannot have higher temperature than water. Temperature of water is multiplied by HeatCurve coefficient as mentioned earlier.<br/>
<code>IF THEN ELSE(Pair1=1, TemperatureOfWater*HeatCurve, BaseTemperature)</code>

===Room heat-up rate===
Each room has its own heat up rate. This heat-up rate depends on size of radiator inside room and Stefan-Boltzman law for heat radiation. Radiated energy is distributed to rooms - each room has its own size (volume), air has different heat capacity, so we have to take it into account. Number 5.67 is Stefan-Boltzman constant.<br/>

<code>
(((R1S*RadiatorSurfacePerPart)*(5.67)*((273.4+R1T)^4)*10^(-8))/(Room1S*AirDensity20)/AirHeatCapacity)
</code>

===Room temperature===
Room heat-up rate is integrated over time. Environment affects air inside rooms and room gets colder. This model has hardwired condition that air cannot be hotter than radiator.<br/>
<code>IF THEN ELSE(Room1Temp + (Room1HR-Room1CR) < R1T, Room1HR-Room1CR, 0)</code>

===Room cool-down rate===
Room environment causes room temperature to decrease over time. Each room is different. I have tried to reflect this fact into constants I have used in cool-down rate computation. I have measured temperature inside each room and then i have set constants according to what I have found. The higher temperature of the room, the higher effect its environment has. <br/>

<code>(Room1Temp-OutsideTemperature)*Ventilation*6</code>

*Room 1-4 - 6
*Room 5 - 12
*Room 6 - 6
*Room 7 - 1

This also models weights of each room affecting living room with thermostat.

=Results=
==Thermostat switching==
Thermostat and gas-boiler switches between 0 and 1 all the time - real system behaves the same way - system always compensates for energy losses. There is no delay in this system, so switching happens instantly.
[[File:Thermostat.png|frame|center|Thermostat switching]]
[[File:HSS-Boiler.png|frame|center|Gas boiler switching]]

==Gas burned==
By changing model parameters, i have tried to find leverage point - most sensitive parameters are outside temperature and ventilation coefficient. By changing temperature from 10 degrees Celsius to 0 degrees Celsius, amount of gas burned doubles.

[[File:HSS-0DG.png|frame|center|Outside temperature set to 0 degrees Celsius]]
[[File:HSS-10DG.png|frame|center|Outside temperature set to 10 degrees Celsius]]

==Water temperature==
Water temperature changes according to the needs of heating system. When there is 0 degrees Celsius outside, gas boiler keeps circulating water heated to 50 degrees Celsius.
[[File:HSS-Water Temperature.png|frame|center|Water temperature, outside temp. 10 degrees Celsius]]
[[File:HSS-Water0.png|frame|center|Water temperature, outside temp. 0 degrees Celsius]]

==Water heating up rate==
Heating-up rate changes according to pair 1 and pair 2 leaving and returning.
[[File:HSS-WHUR.png|frame|center|Water heating-up rate]]
So does amount of the circulating water.
[[File:HSS-WA.png|frame|center|Water amount]]

==Living room==
Living room keeps its temperature according to thermostat settings.
[[File:HSS-LRT.png|frame|center|Living room temperature]]
It's heat-up rate changes according to gas-boiler running.
[[File:HSS-LRHR.png|frame|center|Living room heat-up rate]]

==Room 1 and Room 2==
These rooms are changing its temperature according to pair 1 and pair 2.
[[File:HSS-R1T.png|frame|center|Room 1 temperature]]
[[File:HSS-R2T.png|frame|center|Room 2 temperature]]

=Conclusion=
Heating system has many parameters that changes its behaviour. I was designing this model to get as accurate data as possible, but when I was studying about heating system dynamics, I have realized, that scope of this problematic is so broad that it is not possible to implement this model with enough accuracy. To make this model more accurate i would incorporate:

*Weather statistics and Monte-Carlo generator for base temperature modulation
*Water heat capacity change based on temperature
*Air circulation inside apartment
*Water movement inside tubes
*Room temperature is affected by air circulation and radiator placement - a lot
*Slit-iron heat capacity model
*Passive sun heating through windows
*Possibility to have thermostatic radiator valve regulation
*Equitermal regulation
*Usage of water for shower a dish cleaning
*Random generator of opened windows
*Difference between plastic and wooden windows
*Etc.

This model is good basic study material but it is very inaccurate.
This model cannot predict amount of gas burned during 4 months.
This model can roughly tell that thermostat has good settings - living room temperature is higher just about 2 degrees Celsius and that is exactly what we wanted.

=Code=
[[File:4IT496-knam00-Heating system simulation-2015-2016.mdl]]

File:4IT496-knam00-Heating system simulation-2015-2016.mdl

2016-01-24T21:57:44Z

Knam00: Martin Knapovský - knam00 - Heating system simulation

Martin Knapovský - knam00 - Heating system simulation

File:HSS-R2T.png

2016-01-24T21:16:36Z

Knam00:

File:HSS-R1T.png

2016-01-24T21:16:13Z

Knam00:

File:HSS-LRT.png

2016-01-24T21:12:45Z

Knam00:

File:HSS-LRHR.png

2016-01-24T21:12:18Z

Knam00:

File:HSS-WA.png

2016-01-24T21:08:44Z

Knam00:

File:HSS-WHUR.png

2016-01-24T21:07:33Z

Knam00:

File:HSS-Water0.png

2016-01-24T21:03:49Z

Knam00:

File:HSS-Water Temperature.png

2016-01-24T21:00:49Z

Knam00:

File:HSS-10DG.png

2016-01-24T20:57:57Z

Knam00:

File:HSS-0DG.png

2016-01-24T20:53:02Z

Knam00:

File:HSS-Boiler.png

2016-01-24T20:47:47Z

Knam00: HSS-Boiler.png

HSS-Boiler.png

File:HSS-Water-to-air.png

2016-01-24T20:08:28Z

Knam00: HSS-Water-to-air.png

HSS-Water-to-air.png

File:HSS-Thermostat-to-water.png

2016-01-24T19:16:10Z

Knam00: HSS-Thermostat-to-water.png

HSS-Thermostat-to-water.png

File:Thermostat.png

2016-01-24T19:10:45Z

Knam00: Heating system - Thermostat

Heating system - Thermostat

File:HSS - global.png

2016-01-24T18:07:24Z

Knam00: Heating system simulation - Overview

Heating system simulation - Overview

File:Heating simulation - Apartment Layout.png

2016-01-24T17:01:20Z

Knam00: Heating simulation - Apartment Layout

Heating simulation - Apartment Layout

WS 2015/2016

2016-01-24T16:12:13Z

Knam00: /* Papers */

Semestral papers from winter term 2015/2016. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2015/2016|assignment approved]].

==Simulations==
--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 15:35, 15 January 2016 (CET) [[Maze Solving Robot Simulation]]

--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 22:30, 15 January 2016 (CET) [[Aircraft boarding methods]]

--[[User:OtakarTrunda|OtakarTrunda]] ([[User talk:OtakarTrunda|talk]]) 11:48, 16 January 2016 (CET) [[Ultimatum Game]]

--[[User:Xpokl18|Xpokl18]] ([[User talk:Xpokl18|talk]]) 06:51, 17 January 2016 (CET) [[RetrieverBreeder]]

--[[User:Martin.zima|Martin.zima]] ([[User talk:Martin.zima|talk]]) 10:28, 17 January 2016 (CET) [[Lane-merge optimization]]

--[[User:Xhudj17|Xhudj17]] ([[User talk:Xhudj17|talk]]) 11:25, 17 January 2016 (CET) [[Crossroad simulation]]

--[[User:xtomp36|xtomp36]] ([[User talk:xtomp36|talk]]) 15:33, 17 January 2016 (CET) [[Load-balancing]]

==Papers==
--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 18:14, 22 January 2016 (CET) [[System Archetypes]]

--[[User:Xpokl18|Xpokl18]] ([[User talk:Xpokl18|talk]]) 07:44, 23 January 2016 (CET) [[PPC systems, Ad Rank]]

--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 14:47, 23 January 2016 (CET) [[System Dynamics]]

--[[User:Knam00|Knam00]] ([[User talk:Knam00|talk]]) 17:11, 24 January 2016 (CET) [[Shifting the Burden Archetype]]

Shifting the Burden Archetype

2016-01-24T16:05:56Z

Knam00: Shifting the Burden System Archetype Description and Examples

== Introduction ==
<blockquote>
''An underlying problem generates symptoms that demand attention. But the underlying problem is difficult for people to address, either because it is obscure or costly to confront. So people "shift the burden" of their problem to other solutions—well-intentioned, easy fixes which seem extremely efficient. Unfortunately, the easier "solutions" only ameliorate the symptoms; they leave the underlying problem unaltered. The underlying problem grows worse, unnoticed because the symptoms apparently clear up, and the system loses whatever abilities it had to solve the underlying problem.''

Excerpt From: Peter M Senge. “The Fifth Discipline: The Art and Practice of the Learning Organization: First Edition.”<ref name="fifthdiscipline">Senge, Peter M. (1990), [http://books.google.com/books?id=bVZqAAAAMAAJ The Fifth Discipline], Doubleday/Currency, ISBN 0-385-26094-6</ref> .
</blockquote>

{| class="wikitable" align=right width="200"
! scope="col" width="30px" |Shifting the Burden Archetype
|-
| [[File:Shifting the Burden.png|thumb|centre|upright=0.5|Causal Loop Diagram]]
|-
|}

Shifting the Burden is one of the 16 [[System Archetypes]]. These structures are one of the most important remarks of [[System Dynamics]] and [[Systems Thinking]]. Archetypes are used to detect patterns of behaviour of social systems (businesses, communities, states, economies, etc.) - it can be used as a diagnostic tool of current state or as a tool to look at the future development of the system. As a '''diagnostic tool''' it helps managers to obtain perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a '''prospective tool''' it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached.

[[File:IMG 0157.JPG|thumb|Shifting the Burden Quick Fix Response]]

Shifting the Burden archetype captures the situation when dealing with a problem is solved only by '''temporarily solution'''. This short-term solution also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only '''side effects'''. Shifting the Burden is pervasive on many levels of our society. It is also a key ingredient in ecological matters. We have often avoided confronting the underlying issues because we use quick short-term fixes.

This Systems Archetype was formally identified in Appendix 2 of [[The Fifth Discipline]]<ref name="fifthdiscipline" /> by Peter Senge (1990).

<blockquote>
''If reinforcing and balancing feedback and delays are like the nouns and verbs of systems thinking, then the systems archetypes are analogous to basic sentences or simple stories that get retold again and again. Just as in literature there are common themes and recurring plot lines that get recast with different characters and settings, a relatively small number of these archetypes are common to a very large variety of management situations.''

''The systems archetypes reveal an elegant simplicity underlying the complexity of management issues. As we learn to recognize more and more of these archetypes, it becomes possible to see more and more places where there is leverage in facing difficult challenges, and to explain these opportunities to others.''

Excerpt From: Peter M Senge. The Fifth Discipline: The Art and Practice of the Learning Organization: First Edition.<ref name="fifthdiscipline" />
</blockquote>

== [[The Fifth Discipline]] ==
In 1990 Peter Senge's book The Fifth Discipline<ref name="fifthdiscipline" />: The Art & Practice of the Learning Organization started selling and it began its steady march into the hearts and minds of leaders and students of organizational behavior around the world. The Fifth Discipline was the book that made Peter Senge famous and that made people talk about the concept of the [[Learning Organizations]]. The Journal of Business Strategy, named Senge a '''‘Strategist of the Century’'''; one of 24 men and women who have '''had the greatest impact on the way we conduct business today'''. Additionally, in 1997, Harvard Business Review identified the book as one '''of the most influential management books of the past 75 years'''. The Fifth Discipline is the result of many years research and intervention of hundreds of people. The main inspiration was the analysis of people’s inability to manage complex systems and the focus on decentralizing the role of leadership in organizations so as to enhance the capacity of all people to work efficiently towards common goals <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at
http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.

== [[Systems Thinking]] ==
Systems thinking is a way of seeing the '''connections, links, or relationships between things'''. Instead of seeing parts and pieces of how things happen, it allows the interdependent whole to be appreciated. It tries to comprehend system in holistic nature. It is a process for understanding the '''interrelationships''' among key components of a system, such as: hierarchical relations, process flow, attitudes and perceptions, product quality, sales, production, just in time delivery, cash flow, customer service, delivery, research and development, how decisions are made, and hundreds of other factors. This discipline draws on perceptions and experiences of people from different levels and functions in the organization, providing diverse perspectives for improving the quality of systems thinking. Using '''feedback loops, reinforcing loops, and balancing mechanisms''' helps to '''map out systems''' and the outcomes desired.

== Archetype description ==
Shifting the burden structures are common in our personal as well as organizational lives. They come into play when there are obvious "symptoms of problems" that cry out for attention, and quick and ready "fixes" that can make these symptoms go away, at least for a while.

=== Archetype Causal Loop Diagram ===
[[File:Shifting the Burden.png]]

This archetype consists of two balancing processes, that are trying to adjust or correct the same problem. The top half of diagram represents '''quick fix''', the bottom part represents more '''fundamental response''' to the problem - it has a delay, so its effect takes longer to be evident. Often (but not always), in shifting the burden structures, there is also an additional reinforcing (amplifying) process created by "side effects" of the symptomatic solution. These side effects makes it harder to invoke fundamental solution - the longer we use symptomatic response, the harder it is to make system work fine. For example - if we use drugs to overcome troubles in life, we get into more trouble in future. If we fix problem of dirty dishes by stacking it onto each other, we end up with stockpile we don't want to touch - in global it is easier to clean up dishes after eating, but it is not so comfortable for us, so we just add it to stockpile and stay in our comfort zone.

[[File:Dishes.jpg|thumb|Stacking dishes onto each other and avoiding solution]]

== Shifting the Burden examples ==
A shifting the burden structure is hidden behind many "solutions" which seem to work effectively, but nonetheless leave you with an uneasy feeling that they haven't quite taken care of the problem. Managers may believe in delegating work to subordinates but still rely too much on their own ability to step in and "handle things" at the first sign of difficulty, so that the subordinate never gets the necessary experience to do the job and manager suffering from burn-out. Businesses losing market share to foreign competitors may seek tariff protection and find themselves unable to operate without it. A Third World nation, unable to face difficult choices in limiting government expenditures in line with its tax revenues, finds itself generating deficits that are "financed" through printing money and inflation. Over time, inflation becomes a way of life, more and more government assistance is needed, and chronic deficits become accepted as inevitable. Shifting the burden structures also include food relief programs that "save" farmers from having to grow crops, and pesticides that temporarily remove vermin, but also eliminate natural controls, making it easier for the pest to surge back in the future.

=== Oil Dependence ===
[[File:Shifting the Burden - oil.png]]

When gasoline prices go up, pressure rises for more fuel-efficient cars, then gasoline prices fall and the pressure for low-mileage vehicles vanishes, consumers stop buying those cars, the oil producers celebrate, we remain addicted to oil and prices gradually go up again, petro-dictators get rich, we lose.

=== Human Resources<ref name="fifthdiscipline" /> ===
Busy managers are often tempted to bring in human resource specialists to sort out personnel problems. The HR expert may solve the problem, but the manager's ability to solve other related problems has not improved. Eventually, other personnel issues will arise and the manager will be just as dependent on the HR expert as before. The very fact that the outside expert was used successfully before makes it even easier to turn to the expert again. "We had a new batch of difficulties, so we brought in the personnel specialists again. They are getting to know our people and our situation well, so they are very efficient." Over time, HR experts become increasingly in demand, staff costs soar, and managers' development (and respect) declines.

=== Headache ===
If you have a headache and you take an aspirin and the headache goes away, then you might just be happy with that and not worry about why you had a headache in the first place. Next time you get a headache, you just take another, because it worked in the first place. Pretty soon you will get used to it and always keep aspirin around and never look for a more fundamental solution to headaches. Taking aspirin will make it less
and less likely that you actually adjust your health so that your body works right in the first place. To actually reverse the process and find and apply the fundamental solutions would probably mean that you would suffer through some headaches while the fundamental solutions are worked out and start taking effect. That might mean a change in lifestyle, as in eating or sleeping habits.

=== Software development<ref name="kallokain">Martensson, Henrik: Systems Archetype: Shifting the Burden [online] 13 Aug. 2006. [seen Web. 22 Jan. 2016] Available at: http://kallokain.blogspot.cz/2006/08/systems-archetype-shifting-burden.html</ref>===
Shifting the Burden example in software development is when management tries to apply a brute force solution, like increasing the manpower of a project, or forcing developers to work an excessive amount of overtime, to compensate for low productivity, or poor planning. Often used solution is to add more people to the project. However new people must get used to the working environment, they have to install some software, they have go through some company standards, etc. Few days they won't be much productive and they will also need much more help from other developers. Therefore, the net productivity of the project is reduced. If the system under development is complex, or just messy, there may be several months, before the new developer is up to speed. Until then, the project will not get the productivity increase the management hoped for.

If too many developers have been added at once, there may be major coordination problems. What should all the new people do? The system under development may not have an infrastructure that allows very many people to work on it efficiently. Management usually finds ways to keep everyone busy anyway, in the interest of "cost efficiency". The result is predictable: the software becomes incredibly messy. Consequently, changes become very hard to implement, and the defect count skyrockets. Even if there was an initial improvement in productivity, it is not sustainable.

=== More examples ===
Classic examples of shifting the burden include<ref name="iseesystems">Chichacly, Karim: Shifting the Burden. [online], 22 Dec. 2010. [seen 22 Jan. 2016]. Available at:http://blog.iseesystems.com/systems-thinking/shifting-the-burden</ref>:
*Making up lost time for homework by not sleeping (and then controlling lack of sleep with stimulants)
*Borrowing money to cover uncontrolled spending
*Feeling better through the use of drugs (dependency is the unintended side-effect)
*Taking pain relievers to address chronic pain rather than visiting your doctor to try to address the underlying problem
*Improving current sales by focusing on selling more product to existing customers rather than expanding the customer base
*Improving current sales by cannibalizing future sales through deep discounts
*Firefighting to solve business problems, e.g., slapping a low-quality – and untested – fix onto a product and shipping it out the door to placate a customer
*Repeatedly fixing new problems yourself rather than properly training your staff to fix the problems – this is a special form known as “shifting the burden to the intervener” where you are the intervener who is *Inadvertently eroding the capabilities and confidence of your staff (the unintended side-effect)
*Outsourcing core business competencies rather than building internal capacity (also shifting the burden to the intervener, in this case, to the outsource provider)
*Implementing government programs that increase the recipient’s dependency on the government, e.g., welfare programs that do not attempt to simultaneously address low unemployment or low wages (also shifting the burden to the intervener, in this case, to the government)

== Achieving leverage ==
Dealing with Shifting the Burden archetype requires '''strengthening fundamental response and weakening symptomatic response'''. Strengthening fundamental response requires long-term plan or vision. This vision should be shared (if there is more than one manager in the company). Weakening symptomatic response requires '''willingness to telling the truth about palliatives etc.''' For example: Politicians must admit that the resistance they face to raising taxes comes from the perception that the government is corrupt. Until they deal credibly with perceived corruption, they will neither be able to raise taxes nor reduce spending.<ref name="fifthdiscipline" />. Very good illustration of the principles of leverage can be seen in alcoholism and drug treatment programs - they must face their addiction and they are also offered training and support groups, which help them face their problems in the future.

'''Symptomatic solutions are often needed''' - treating a person suffering from a disease created by smoking or drinking. They should be always '''combined with rehabilitating the capacity for fundamental solution'''.

== Management principle ==
<blockquote>''Beware the symptomatic solution. Solutions that address only the symptoms of a problem, not fundamental causes, tend to have short-term benefits at best. In the long term, the problem resurfaces and there is increased pressure for symptomatic response. Meanwhile, the capability for fundamental solutions can atrophy''

Excerpt From: Peter M Senge. he Fifth Discipline: The Art and Practice of the Learning Organization: First Edition <ref name="fifthdiscipline" />
</blockquote>

== See also ==
*[[System Dynamics]]
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]

== References ==

File:Dishes.jpg

2016-01-24T15:34:09Z

Knam00: Knam00 uploaded a new version of File:Dishes.jpg

Shifting the Burden - Dishes

File:Dishes.jpg

2016-01-24T15:32:25Z

Knam00: Shifting the Burden - Dishes

Shifting the Burden - Dishes

File:IMG 0157.JPG

2016-01-24T14:49:59Z

Knam00: Shifting the Burden Quick Fix

Shifting the Burden Quick Fix

File:Shifting the Burden.png

2016-01-24T10:38:25Z

Knam00: Knam00 uploaded a new version of File:Shifting the Burden.png

Shifting the Burden Causal Loop Diagram

File:Shifting the Burden.png

2016-01-24T10:38:14Z

Knam00: Knam00 uploaded a new version of File:Shifting the Burden.png

Shifting the Burden Causal Loop Diagram

File:Shifting the Burden.png

2016-01-24T10:37:53Z

Knam00: Knam00 uploaded a new version of File:Shifting the Burden.png

Shifting the Burden Causal Loop Diagram

File:Shifting the Burden - oil.png

2016-01-24T10:32:30Z

Knam00: Shifting the Burden archetype - Oil example

Shifting the Burden archetype - Oil example

File:Shifting the Burden.png

2016-01-24T10:24:30Z

Knam00: Shifting the Burden Causal Loop Diagram

Shifting the Burden Causal Loop Diagram

Assignments WS 2015/2016

2015-12-16T20:13:27Z

Knam00:

{{DISPLAYTITLE:Assignments WS 2015/2016}}

{{Ambox
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Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.
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{{Ambox
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Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].
</div>
}}

{{Ambox
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| text = <div>
In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.
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}}

= Assignments =

[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 15:35, 29 November 2015 (CET) Just to warn you in advance - topics like roulette, lottery and other simulations based purely on uniform randomnes '''will not be accepted''' as Monte Carlo simulation.

--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 03:32, 11 December 2015 (CET)
I have changed my topic to Chemicals manufacturing process simulation

==mand01: Chemicals manufacturing process simulation==
--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 21:24, 14 December 2015 (CET)
===Introduction===
The simulation shows how pharmaceutical companies' manufacturing process is held. In this case manufacturing process is a chemical batch production process which has shared resources. There are two basic systems included: Ordering system and Production systems. The first one generates orders of chemicals to be produced with different demands on a production. As this simulation is for educational purposes, I have decided to simplify the model to 2 types of drugs produced. The production system is assigning reactors to a particular order and processing it. We have shared resources and one of the main shared resource is a reactor. One reactor processing one order. Once an order is received we align each per order and then production begins, which include several steps as adding water, heating water, adding particles, stiring and draining. After all production steps we release the reactor for the next order.

===Problem definition===
This model explores how to solve resource allocation problem.

We need to process 100 orders (60 orders for drug A and 40 for drug B) in a month. Reactors works 12 hours a day. We obtain 5 reactors, we can add water to two reactors simultaneously and heat two reactors at a time.

'''Drug A''': adding water 2 hours, heating 3 hours, adding particles 1 hour, stiring 3 hours and draining 3 hours

'''Drug B''': adding water 1 hour, heating 2 hours, adding paticles 30 min, stiring 5 hours and draining 3 hours

Do we need an additional reactor to acomplish this task?

===Method===
Tool to be used - ''Simprocess''

As stated [[How_to_deal_with_the_simulation_assignment|here]], for discrete event simulations a real problem from real business with real data is mandatory.

== mand01: Aircraft boarding methods ==
--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 04:41, 16 December 2015 (CET)
===Introduction ===
This simulation explores different methods of aircraft boardings. Airports serves thousands of people every day and one of the main problems they meet is the efficiant boarding of passengers.

Exists several methods of boarding:

- random (which is widely used, the most common): people just taking their seats without any specific order

- WMA (Windows-Middle-Aisle) - boarding is divided into three groups: seats near the windows, middle seats and aisle seats. The first boarding group is windows then middle and finally the aisle group.

- Back-front WMA: we divide groups in the same way as in WMA, but the only difference, that we sort passengers in decreasing order. For each group boadring starts from back of the plane.

=== Goal ===
To simulate different boarding scenarious and try to find out the optimal one.

=== Method ===
Environment: NetLogo

Agents: passengers (row number, seat number, isSitting, isMoving, luggage, luggageLoadTime, orderingNumber)

Simulation ends when all passengers are sitting (isSitting == true). Every agent is independent, random number of passengers are with luggage, luggage has the same size.

For simulation we can chose the boarding method, probability of passengers with luggage. Other parameters are fixed. Plane: 18 rows x 6 seats (3 per each side).

'''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:47, 16 December 2015 (CET)

[[xpokl18]]

==knam00:Heating system simulation==
===Intro===
<div>
Most households in the Czech Republic have mains gas central heating. This is a so-called ‘wet system’, which means a gas-fired boiler heats water to provide central heating through radiators and hot water through the taps in your home.

Some houses that aren’t connected to the gas network can use electrical heating or liquid petroleum gas (LPG) or heating oil, which work in a similar way to gas central heating, although LPG and oil are delivered by road and stored in a tank, which you may have to buy or rent from your supplier.

Gas is a highly efficient fuel, so you get a good return on every unit of energy. Modern condensing boilers, which use hot flue gases that are wasted in a standard boiler, have very high efficiency. Some are now 90% or more efficient.
</div>
<div>
===Simulation design===
====Layout====
[http://imageshack.com/a/img910/6936/OIUdr1.png Preview Heating system]
====People====
There are 6 people using this flat. Each person periodically comes and leaves.
Pair 1 comes on monday and leaves on wednesday
Pair 2 comes on monday and leaves on friday
There are also 2 boys sharing another room - The first one stays here for the whole week and the second one leaves on saturday and comes on monday.

====Gas boiler====
We own 25kW condensing boiler with 95% efficiency.
Boiler is controlled with thermostat.

====Thermostat====
Thermostat is placed in the living room - it is set to 22 degrees Celsius from 7:00 to 23:00 and to 19 degrees Celsius from 23:00 to 7:00.

====Rooms====
There are 3 separated rooms used privately, one living room, kitchen, bathroom, storage room and toilet.
He have high ceilings, so volume of rooms is really big.

Private rooms - 84mˆ3 x 3
Living room - 168mˆ3
Kitchen - 84mˆ3
Bathroom - 21mˆ3
Storage room - 21mˆ3
Toilet - 21mˆ3

====Radiators====
We have old radiators made from cast-iron. Each part of this radiator is 600mm high and 200mm deep, its surface is 0,31mˆ2, its capacity is 1,7L.
Each room (except storage room) has its own radiator.
In total, there are 5 radiators with 30 parts and 2 radiators with 15 parts.

====Heating system====
Consists of radiators and tubes. Total amount of water inside heating system is 255L (5 big radiators) + 51L (2 smaller radiators) + 61L (Amount of water inside tubes - 108m of tubes in total) = 367L.

====System dynamics====
Water inside heating system gets heated - it takes 4181J to heat up 1L of water to 1 degree Celsius. Then radiators are heated (416J for 1kg of cast-iron to heat-up to 1 degree Celsius), then air is heated according to Stefan-Boltzman law. Heat is distributed among the flat. According to the people in the flat, radiators are turned on or off. Thermostat is inside the living room a living room gets 30% of heat from rooms connected to it. Thermostat stops heating system when requirement are satisfied. Accumulated heat stays in heating system and slowly degrades. I would like to measure amount of gas used for heating system for the period 4 months.

The system i describe doesn't count with using hot water for shower.
</div>
<div>
I would like to ask you for software recommendation - not sure if i should use simprocess or vensim.
</div>
<div>
Martin Knapovský, knam00@vse.cz
</div>

:This could be an interesting topic, but please, finish the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:44, 13 December 2015 (CET)

::From my point of view, systems' dynamics could be a good one. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:49, 15 December 2015 (CET)
::: System dynamics seems fine with me.'''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:05, 16 December 2015 (CET)

== Christmas market ==
<div>
I´m thinking about simulation of Christmas market, on some square as place for vendors. There will be two kind of vendors – with refreshments and with decorations. There will be two entities – vendors and customers.
In first round I want place there some number of customers (for example twenty) and some number of vendors.
Each customer has to buy at least one decoration as a present for somebody. In next rounds each customer can decide if he want to leave a market, buy other present or buy something to eat/drink. Each customer can buy no more than five presents.
Every minute will come five new customers.
There will be a temperature as global variable.
If the temperature is too low, customer will decide, if he/she wants to go home or buy something to eat/drink to get warm.
If customer is on a market for so long, he/she needs to buy something to eat or drink either.
Because there will be more than one vendor of each kind of goods, customer will preference vendor by the distance.
</div><div>
My goal is to find an optimal number of vendors and its structure based on the given temperature.
</div><div>
For my simulation I decide to use NetLogo.
</div>
<div>--[[User:Xhejk15|Xhejk15]] ([[User talk:Xhejk15|talk]]) 20:58, 13 December 2015 (CET)</div>
:This could be interesting if you would use real data. Otherwise it is kinda academic exercise. And by the way, it seems to be rather a discrete event simulation than an agent model. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:52, 15 December 2015 (CET)

== xsedd07: assignment ==
Conference and meeting center layout in '''NetLogo'''

This simulation would display how the crowds (and individuals) would behave within a conference center. The building would have a set plan, what would be changing could be the exits, the information desks, the buffets, the bars, the exhibitions, the lectures, the meetings, the benches and the free space. The visitors and participants would move around based on a schedule and free will, at this point I am not sure to which extent is it wise to design an AI which would control each person as an individual. Hopefully such AI can be made with NetLogo spending just reasonable effort. The simulation extent (regarding total amount of people, rooms...) will be adjusted so that it can be easily overviewed but not yet too simplified.

The layout will be transformed multiple times, its efficiency will be judged by density of the crowd and queues. Individual buffets, bars e.t.c. should not be abandoned while the others are flooded. Simulation will include staff and toilets to make the environment look more real. At the moment I am open to implementing a possible terrorist attack, which could also be an alternative for this assignment.

The simulation should track the number of people in lecture halls, in queues and in any otherwise interesting state such as boredom or stress.
: I would make it simpler. Don't bother simulating all the day, simulate just as people are coming for the particular event/presentation. Simulate how crowd are assembling at doors, toilets, how people avoiding each other to get their seats. Etc. Just the time let's say +-10 minutes around the beginning of the event. Of course, you need to simulate relative intelligent movement of agents. If you agree, than it is '''approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:58, 15 December 2015 (CET)

== xtomp36: assigment ==
===Hosting load-balancing simulation===

A hosting company with it’s own infrastructure is using so called "load balancing" to distribute the overall load between multiple servers (hardware) and “high-availability” to minimize service down-time.

'''Software used''' <br />
- SIMPROCESS

'''Possible hosting services'''<br />
- Web hosting<br />
- VPS<br />
- Communication server (TS 3)

The simulation should also consider critical situation like off-line server or unavailability (for example due to D-DOS attack) of the entire server location (datacenter). In with case the traffic should be re-routed to another location (there are two hosting locations).<br />
I am going to use real data from my own hosting environment.

'''Variables'''<br />
- Number of servers<br />
- Probability of hardware malfunction<br />
- Probability of software malfunction<br />
- Probability of entire location unavailability<br />
- Service users (website visitors, VPS users, TS 3 clients)

'''Other'''<br />
In addition there are other devices necessary to enable LB and HA, like a switch. In case of HA enabled there must be at least two same switches at one time to achieve redundancy.

'''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:59, 15 December 2015 (CET)

== Predator-Prey simulation ==
Few weeks ago a came across [http://www.natureworldnews.com/articles/6296/20140308/deer-overpopulation-threat-forest-growth-researchers.htm an interesting article] about deer overpopulation and how this is slowing down forest succession or natural establishment. 'The study was conducted on Cornell land near Freese Road in Ithaca,' as stated in the article.<br />
I would like to make a simulation of the predator-prey concept on this topic. We would be able to simulate the growth of deer, wolf population, the state of the 2 types of vegetation in the forest and how many deers and wolves would be optimal to keep the original forest vegetation from disappearing.<br />
Basically there will be total of 4 entities - 2 agents and 2 patch types:<br />
Deer - agent, consuming the native forest flora,<br />
Wolves - agent, the predator specie that hunts the deer specie,<br />
Forest vegetation - native forest flora that deer specie prefers to consume,<br />
Foreign vegetation - the more the deers eat the native forest vegetation the more this foreign flora thrives.<br />

NetLogo will be used for this simulation. I understand it might be a little more complex because this simulation would not be simulating only the deer population but the predator (wolves) as well. It would also simulate the interactions between deer and the native forest flora, wolves hunting deers and the foreign flora taking over the forest until there is little or none of the original forest vegetation. <br />
There will be some global variables such as the initial number of deers and wolves; deer, wolf, forest and foreign flora reproduction rates and rules of interaction for these entities.

--[[User:Xnovs00|Xnovs00]] ([[User talk:Xnovs00|talk]]) 21:57, 13 December 2015 (CET)

There is already so many predator-prey simulations (e.g. in Netlogo itself is at least one or two) that I am a bit reluctant to approve another one. Or please, let me know what is different on your particular case? 22:03, 15 December 2015 (CET)

== xhudj17: assigment ==
===Crossroad simulation===

This simulation should simulate trafic (cars and trams) and pedestrians on crossroad of roads Sokolovska, Jecna and Legerova located on I.P.Pavlova square in Prague.

'''Software used''' <br />
- NetLogo

'''Goal'''<br />

The simulation should try to optimize the trafic lights to reach lowest waiting times possible and to secure safety and fluency of the trafic.

'''Variables'''<br />
- Propability of incoming cars from each direction<br />
- Probability of incoming pedesterians<br />
- Probability of incoming trams<br />
- Light intervals<br />

'''Situation desctription'''<br />
The crossroad is combining 3 main streets and 3 smaller streets with several pedestrians crossing and two tram crossing. There are 2 separate trafic lights to controll the traffic.

Jan Hudecek, XHUDJ17, 14/12/2015 13:50

'''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:04, 15 December 2015 (CET)

== xzimm00: Smart car traffic optimization ==

The model presented by this paper is simulating the situation of car multi-lane merging.

=== Goal ===

The primary goal of the simulation is to create a plausible real-world model of car traffic jams caused by agents operating in an inefficient way and environment that does not make the best of today's technologies that could help control the traffic flow, and to measure the possible improvement if smarter systems were employed.
The author takes into account the inefficiencies of the agents (human error...) and compares this with a (possibly better) solution using automated driver agents, always utilizing an (ideally) optimal (precomputed) traffic flow.

=== Variables ===
* Car inflow (cars/minute, distributed across the lanes)
* Number of lanes (starting and final, after the lane merge)
* Car speed (varying across the different road segments - i.e. max speed may be limited after the merge because of a traffic obstruction, for example a car accident or roadworks)
* Agents' merging strategy preference (i.e. late vs early merge)
* Agent inefficiency factor (reactions time, premature slowing down, wrong merging strategy used, varying speed, overcautiousness...)
* Amount of kept safety factor (for both human-based and automatic computer-based agent driven cars, so called 'defensive driving', expecting a failure of the others - i.e. mainly the distance kept between the cars)

=== Methods used ===
* Software used: NetLogo

=== Expected results ===
Based on the results of the simulation, it should be possible to measure the possible improvement (%) if automated car-driving agents were employed in place of humans in different critical situations of lane mergers.

(Martin Zima, xzimm00)
--[[User:Martin.zima|Martin.zima]] ([[User talk:Martin.zima|talk]]) 18:31, 14 December 2015 (CET)

'''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:06, 15 December 2015 (CET)

== Maze Solving Robot Simulation ==
=== Assignment (xkrep33) ===
==== Goal ====
The primary goal is to create an autonomous robot, which is able to find way out of the maze. The robot will be programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that will include randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.

Secondary objective will be to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.
==== Software ====
For simulation will be used NetLogo software (2D version).
==== Autonomous robot ====
Robot will be able to move up, down, left and right (viewed from above). He should be able to find the way out without any intervention. Robot will be able to move in any environment (maze) where exists at least one posible way out.
==== Environment (maze) ====
Environment will be represented by the World in NetLogo. The World will consists of black, grey and green patches. Black patches will represents the path where robot can move. The grey patches will represent walls, robot cannot enter them and finally the green patches will be the door out of the maze.

More than one enviroment will be provided. Environments will vary to prove that the robot is able to avoid an infinite loop situation.

--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 20:46, 14 December 2015 (CET)

Sounds interesting. '''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:08, 15 December 2015 (CET)

== Simulation of Aqua park ==
=== Assignment (xjanj58) ===
==== Description ====

The purpose of this simulation will be representation of Aqua park with indoor and outdoor pool. The swimmers will have random preferences and they will go to swim in one of two pools. With the pools gradually filling with swimmers it will have negative effect on motivation of each swimmer and it will show change of their preferences. Based on weather and temperature swimmers will also change their preferences between outdoor and indoor pool. Next parameter could be water purity, with high level of contamination produced by swimmers their motivation will lower and on the contrary.

==== Goals ====
Simulation will be set for period of one calendar year and at the end we can compare, if its profitable for Aquapark to have both pools open for a whole year. It will be cheaper to run outdoor pool, but bad weather will have much higher impact on outside swimmers. Next goal will be confirmation of assumption, that in summer people prefer outdoor pool and on the contrary in winter indoor pool.

==== Used software====
NetLogo

--[[User:xjanj58|xjanj58]] ([[User talk:xjanj58|talk]]) 22:36, 14 December 2015 (CET)

Sounds good, however you will probably struggle with soft parameters like "the willingness to swim in dirty water". How do you plan to deal with it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:23, 15 December 2015 (CET)

--- How about simplification in a way, that agents will produce "dirtyness" and there will be fix line, after which pool will be so dirty that nobody wants to swim in it. Indoor pool will have regular cleaning maintaince wich will lover level of dirtiness, but outside pool will only be cleaned after crossing this limit. It will take some time, lets say day or two to clean it, and then swimmers could go in again. [[User:xjanj58|xjanj58]] ([[User talk:xjanj58|talk]]) 9:56, 16 December 2015 (CET)

:: But, again, how do you turn this into specific numbers? How you will measure it? How do you know how quickly a pool gets dirty? And mainly, how will you measure the feeling of people about the quality of water. Those soft parameters are always a pain. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:52, 16 December 2015 (CET)
::--- I was thinking that agents will have parametr called e.g. "mood", and it will be interval between 0 and 10. Then for every new swimmer in the pool will this parametr go down and when it hit 0, then this particular agent will exit the pool. For dirtines, i was thinking thet pool will have some limit, for example 100, and every fifth move of the agent in the pool will fill this parametr by 1. When it reaches 100, the pool will be dirty. [[User:xjanj58|xjanj58]] ([[User talk:xjanj58|talk]]) 20:17, 16 December 2015 (CET)

::: Yes, I understand. This is exactly what I call "an academic exercise". How do you know that 0-10 is a correct interval to measure mood? How do you know that this is a linear function? Why it should go down by 1 (and not by 3 or 5 or 0.5) and why it should be the same for everyone? The same for the quality of water. Sure, you can make your model this way. But, will it be still in touch with reality? And if not, what's the point then? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:01, 16 December 2015 (CET)

== Otakar Trunda: The Ultimatum Game ==

Agent-based simulation to analyze the socio-economical experiment known as The Ultimatum Game.

=== Game rules ===

The rules can be found at [https://en.wikipedia.org/wiki/Ultimatum_game]. During the game two players interact in order to divide certain amount of money (e.g. $1000 000) between them. At the beginning, the first player proposes the ratio of division (like $900 000 for himself and $100 000 for the other player, or any other ratio). Then the second player has two choices - he can either accept - in which case the players receive the agreed amounts of money, or he can reject - in which case no player receive any money.

=== Goals ===

There are several questions that can be studied about the Ultimatum Game. For example:

* how would people behave? (assuming the players don't know each other)
* what amount of money should the first player propose to the second? (to maximize his own profit)
* what amount of money should the second player accept?
* is it rational for the second player to reject any proposal? Why?
* if the game is played repeatedly and different players use different strategies, what strategy maximizes agents' long-run profit?
* if agents' strategies can evolve in time, can altruistic strategies emerge?
* are there evolutionary stable strategies? Which ones?

=== Method ===

In the system there will be agents representing players, each of them will play according to his strategy. The strategy of the player tells how much he will offer (should he be the first player in the game) and what is the least amount he will accept (should he be the second player in the game). In each simulation step, two agents will be selected randomly and play the game. Their score will be adjusted by the result of the game (i.e. if the second agent accepts, they will gain money according to the proposal, otherwise the score remains unchanged). Agents' successfulness is determined by their score.

After a large number of simulation steps (like 1000 000 of more), we should be able to determine how the strategy affects the final score of agents (i.e. what strategies are good).

=== Evolution of strategies ===

The system can be modified such that agents' strategies will change during the simulation to emulate biological evolution. In this case, successful agents will multiply in the system (i.e. more agents will use good strategies) and non-successful agents will die-out. During the reproduction of agents, their strategies will be slightly modified - the offspring will use a slightly different strategy then the parent. This way, the system should evolve to some kind of equilibrium where either all agents use very similar strategy (static equilibrium), or different strategies survive to compete with each other (dynamic equilibrium).

After a large number of simulation steps, we should be able to observe whether altruistic strategies emerge (strategies, that offer "fair" amounts and reject small offers) and determine the existence of stable strategies (strategies that can't be taken advantage of by "parasitic" strategies).

=== Software ===

Tailor-made in C# using Visual Studio.

[[User:Trunda|Trunda]] ([[User talk:Trunda|talk]]) 17:35, 15 December 2015 (CET)

The problem is well known. However, you will probably struggle with simulating irrationality and some other particular problems. The model will tend to be either pretty complex, almost hard to manage or too trivial. I would think twice and explain how exactly you will e.g. deal with the particular strategies.
Also, why do you want to use C# instead of a certain agent framework? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:39, 15 December 2015 (CET)

I guess it is a well-known problem, but it seems interesting and I don't know of any similar problem that wouldn't be already well known. Can you perhaps recommend some?
How to deal with strategies: the strategy will be a pair of real numbers, lets denote them A and B from <0,1>. A will denote how much the strategy offers (e.g. A = 0.4 means that the player will offer 40% of the total amount to the second player) and B denotes how much at least the strategy accepts (e.g. B = 0.2 means that the player will reject any offer smaller than 20% of the amount and accept offers that are 20% or larger). Then the evolution can be done by a simple genetic algorithm on these pairs, fitness of the agent would be determined by its score.
I hope this setting is not too trivial, it can be further extended by adding a "space dimension" meaning that agents will only play the game with agents that are near them and during the reproduction the offspring will also be created near the parent. In this case there should emerge localized groups of agents using the same strategy, some groups should successively expand, others diminish.
I could use NetLogo, but I think it will be easier to do in C#. For example I would need to visualize the performance of the strategies - the score based on numbers A and B (mentioned earlier). This can be done in a form of a 3D graph, which I can create in C# easily using the proper library. I will also need to compute various statistics of the agents' scores to properly interpret the results (like moments and p-values) which again can be done easily using proper C# framework. It might be able to do these in NetLogo as well, but that would take me much more time :) [[User:Trunda|Trunda]] ([[User talk:Trunda|talk]]) 16:35, 16 December 2015 (CET)
::Good. According to what you have mentioned, you will also need to deal with irrationality. How you will do that? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:56, 16 December 2015 (CET)

Assignments WS 2015/2016

2015-12-16T20:12:39Z

Knam00: /* knam00:Heating system simulation */

{{DISPLAYTITLE:Assignments WS 2015/2016}}

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Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.
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Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].
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In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.
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= Assignments =

[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 15:35, 29 November 2015 (CET) Just to warn you in advance - topics like roulette, lottery and other simulations based purely on uniform randomnes '''will not be accepted''' as Monte Carlo simulation.

--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 03:32, 11 December 2015 (CET)
I have changed my topic to Chemicals manufacturing process simulation

==mand01: Chemicals manufacturing process simulation==
--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 21:24, 14 December 2015 (CET)
===Introduction===
The simulation shows how pharmaceutical companies' manufacturing process is held. In this case manufacturing process is a chemical batch production process which has shared resources. There are two basic systems included: Ordering system and Production systems. The first one generates orders of chemicals to be produced with different demands on a production. As this simulation is for educational purposes, I have decided to simplify the model to 2 types of drugs produced. The production system is assigning reactors to a particular order and processing it. We have shared resources and one of the main shared resource is a reactor. One reactor processing one order. Once an order is received we align each per order and then production begins, which include several steps as adding water, heating water, adding particles, stiring and draining. After all production steps we release the reactor for the next order.

===Problem definition===
This model explores how to solve resource allocation problem.

We need to process 100 orders (60 orders for drug A and 40 for drug B) in a month. Reactors works 12 hours a day. We obtain 5 reactors, we can add water to two reactors simultaneously and heat two reactors at a time.

'''Drug A''': adding water 2 hours, heating 3 hours, adding particles 1 hour, stiring 3 hours and draining 3 hours

'''Drug B''': adding water 1 hour, heating 2 hours, adding paticles 30 min, stiring 5 hours and draining 3 hours

Do we need an additional reactor to acomplish this task?

===Method===
Tool to be used - ''Simprocess''

As stated [[How_to_deal_with_the_simulation_assignment|here]], for discrete event simulations a real problem from real business with real data is mandatory.

== mand01: Aircraft boarding methods ==
--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 04:41, 16 December 2015 (CET)
===Introduction ===
This simulation explores different methods of aircraft boardings. Airports serves thousands of people every day and one of the main problems they meet is the efficiant boarding of passengers.

Exists several methods of boarding:

- random (which is widely used, the most common): people just taking their seats without any specific order

- WMA (Windows-Middle-Aisle) - boarding is divided into three groups: seats near the windows, middle seats and aisle seats. The first boarding group is windows then middle and finally the aisle group.

- Back-front WMA: we divide groups in the same way as in WMA, but the only difference, that we sort passengers in decreasing order. For each group boadring starts from back of the plane.

=== Goal ===
To simulate different boarding scenarious and try to find out the optimal one.

=== Method ===
Environment: NetLogo

Agents: passengers (row number, seat number, isSitting, isMoving, luggage, luggageLoadTime, orderingNumber)

Simulation ends when all passengers are sitting (isSitting == true). Every agent is independent, random number of passengers are with luggage, luggage has the same size.

For simulation we can chose the boarding method, probability of passengers with luggage. Other parameters are fixed. Plane: 18 rows x 6 seats (3 per each side).

'''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:47, 16 December 2015 (CET)

[[xpokl18]]

Introduction

=Problem definition=

=Method=

=Model=

=Results=

=Conclusion=

=Code=

==knam00:Heating system simulation==
===Intro===
<div>
Most households in the Czech Republic have mains gas central heating. This is a so-called ‘wet system’, which means a gas-fired boiler heats water to provide central heating through radiators and hot water through the taps in your home.

Some houses that aren’t connected to the gas network can use electrical heating or liquid petroleum gas (LPG) or heating oil, which work in a similar way to gas central heating, although LPG and oil are delivered by road and stored in a tank, which you may have to buy or rent from your supplier.

Gas is a highly efficient fuel, so you get a good return on every unit of energy. Modern condensing boilers, which use hot flue gases that are wasted in a standard boiler, have very high efficiency. Some are now 90% or more efficient.
</div>
<div>
===Simulation design===
====Layout====
[http://imageshack.com/a/img910/6936/OIUdr1.png Preview Heating system]
====People====
There are 6 people using this flat. Each person periodically comes and leaves.
Pair 1 comes on monday and leaves on wednesday
Pair 2 comes on monday and leaves on friday
There are also 2 boys sharing another room - The first one stays here for the whole week and the second one leaves on saturday and comes on monday.

====Gas boiler====
We own 25kW condensing boiler with 95% efficiency.
Boiler is controlled with thermostat.

====Thermostat====
Thermostat is placed in the living room - it is set to 22 degrees Celsius from 7:00 to 23:00 and to 19 degrees Celsius from 23:00 to 7:00.

====Rooms====
There are 3 separated rooms used privately, one living room, kitchen, bathroom, storage room and toilet.
He have high ceilings, so volume of rooms is really big.

Private rooms - 84mˆ3 x 3
Living room - 168mˆ3
Kitchen - 84mˆ3
Bathroom - 21mˆ3
Storage room - 21mˆ3
Toilet - 21mˆ3

====Radiators====
We have old radiators made from cast-iron. Each part of this radiator is 600mm high and 200mm deep, its surface is 0,31mˆ2, its capacity is 1,7L.
Each room (except storage room) has its own radiator.
In total, there are 5 radiators with 30 parts and 2 radiators with 15 parts.

====Heating system====
Consists of radiators and tubes. Total amount of water inside heating system is 255L (5 big radiators) + 51L (2 smaller radiators) + 61L (Amount of water inside tubes - 108m of tubes in total) = 367L.

====System dynamics====
Water inside heating system gets heated - it takes 4181J to heat up 1L of water to 1 degree Celsius. Then radiators are heated (416J for 1kg of cast-iron to heat-up to 1 degree Celsius), then air is heated according to Stefan-Boltzman law. Heat is distributed among the flat. According to the people in the flat, radiators are turned on or off. Thermostat is inside the living room a living room gets 30% of heat from rooms connected to it. Thermostat stops heating system when requirement are satisfied. Accumulated heat stays in heating system and slowly degrades. I would like to measure amount of gas used for heating system for the period 4 months.

The system i describe doesn't count with using hot water for shower.
</div>
<div>
I would like to ask you for software recommendation - not sure if i should use simprocess or vensim.
</div>
<div>
Martin Knapovský, knam00@vse.cz
</div>

:This could be an interesting topic, but please, finish the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:44, 13 December 2015 (CET)

::From my point of view, systems' dynamics could be a good one. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:49, 15 December 2015 (CET)
::: System dynamics seems fine with me.'''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:05, 16 December 2015 (CET)

== Christmas market ==
<div>
I´m thinking about simulation of Christmas market, on some square as place for vendors. There will be two kind of vendors – with refreshments and with decorations. There will be two entities – vendors and customers.
In first round I want place there some number of customers (for example twenty) and some number of vendors.
Each customer has to buy at least one decoration as a present for somebody. In next rounds each customer can decide if he want to leave a market, buy other present or buy something to eat/drink. Each customer can buy no more than five presents.
Every minute will come five new customers.
There will be a temperature as global variable.
If the temperature is too low, customer will decide, if he/she wants to go home or buy something to eat/drink to get warm.
If customer is on a market for so long, he/she needs to buy something to eat or drink either.
Because there will be more than one vendor of each kind of goods, customer will preference vendor by the distance.
</div><div>
My goal is to find an optimal number of vendors and its structure based on the given temperature.
</div><div>
For my simulation I decide to use NetLogo.
</div>
<div>--[[User:Xhejk15|Xhejk15]] ([[User talk:Xhejk15|talk]]) 20:58, 13 December 2015 (CET)</div>
:This could be interesting if you would use real data. Otherwise it is kinda academic exercise. And by the way, it seems to be rather a discrete event simulation than an agent model. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:52, 15 December 2015 (CET)

== xsedd07: assignment ==
Conference and meeting center layout in '''NetLogo'''

This simulation would display how the crowds (and individuals) would behave within a conference center. The building would have a set plan, what would be changing could be the exits, the information desks, the buffets, the bars, the exhibitions, the lectures, the meetings, the benches and the free space. The visitors and participants would move around based on a schedule and free will, at this point I am not sure to which extent is it wise to design an AI which would control each person as an individual. Hopefully such AI can be made with NetLogo spending just reasonable effort. The simulation extent (regarding total amount of people, rooms...) will be adjusted so that it can be easily overviewed but not yet too simplified.

The layout will be transformed multiple times, its efficiency will be judged by density of the crowd and queues. Individual buffets, bars e.t.c. should not be abandoned while the others are flooded. Simulation will include staff and toilets to make the environment look more real. At the moment I am open to implementing a possible terrorist attack, which could also be an alternative for this assignment.

The simulation should track the number of people in lecture halls, in queues and in any otherwise interesting state such as boredom or stress.
: I would make it simpler. Don't bother simulating all the day, simulate just as people are coming for the particular event/presentation. Simulate how crowd are assembling at doors, toilets, how people avoiding each other to get their seats. Etc. Just the time let's say +-10 minutes around the beginning of the event. Of course, you need to simulate relative intelligent movement of agents. If you agree, than it is '''approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:58, 15 December 2015 (CET)

== xtomp36: assigment ==
===Hosting load-balancing simulation===

A hosting company with it’s own infrastructure is using so called "load balancing" to distribute the overall load between multiple servers (hardware) and “high-availability” to minimize service down-time.

'''Software used''' <br />
- SIMPROCESS

'''Possible hosting services'''<br />
- Web hosting<br />
- VPS<br />
- Communication server (TS 3)

The simulation should also consider critical situation like off-line server or unavailability (for example due to D-DOS attack) of the entire server location (datacenter). In with case the traffic should be re-routed to another location (there are two hosting locations).<br />
I am going to use real data from my own hosting environment.

'''Variables'''<br />
- Number of servers<br />
- Probability of hardware malfunction<br />
- Probability of software malfunction<br />
- Probability of entire location unavailability<br />
- Service users (website visitors, VPS users, TS 3 clients)

'''Other'''<br />
In addition there are other devices necessary to enable LB and HA, like a switch. In case of HA enabled there must be at least two same switches at one time to achieve redundancy.

'''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:59, 15 December 2015 (CET)

== Predator-Prey simulation ==
Few weeks ago a came across [http://www.natureworldnews.com/articles/6296/20140308/deer-overpopulation-threat-forest-growth-researchers.htm an interesting article] about deer overpopulation and how this is slowing down forest succession or natural establishment. 'The study was conducted on Cornell land near Freese Road in Ithaca,' as stated in the article.<br />
I would like to make a simulation of the predator-prey concept on this topic. We would be able to simulate the growth of deer, wolf population, the state of the 2 types of vegetation in the forest and how many deers and wolves would be optimal to keep the original forest vegetation from disappearing.<br />
Basically there will be total of 4 entities - 2 agents and 2 patch types:<br />
Deer - agent, consuming the native forest flora,<br />
Wolves - agent, the predator specie that hunts the deer specie,<br />
Forest vegetation - native forest flora that deer specie prefers to consume,<br />
Foreign vegetation - the more the deers eat the native forest vegetation the more this foreign flora thrives.<br />

NetLogo will be used for this simulation. I understand it might be a little more complex because this simulation would not be simulating only the deer population but the predator (wolves) as well. It would also simulate the interactions between deer and the native forest flora, wolves hunting deers and the foreign flora taking over the forest until there is little or none of the original forest vegetation. <br />
There will be some global variables such as the initial number of deers and wolves; deer, wolf, forest and foreign flora reproduction rates and rules of interaction for these entities.

--[[User:Xnovs00|Xnovs00]] ([[User talk:Xnovs00|talk]]) 21:57, 13 December 2015 (CET)

There is already so many predator-prey simulations (e.g. in Netlogo itself is at least one or two) that I am a bit reluctant to approve another one. Or please, let me know what is different on your particular case? 22:03, 15 December 2015 (CET)

== xhudj17: assigment ==
===Crossroad simulation===

This simulation should simulate trafic (cars and trams) and pedestrians on crossroad of roads Sokolovska, Jecna and Legerova located on I.P.Pavlova square in Prague.

'''Software used''' <br />
- NetLogo

'''Goal'''<br />

The simulation should try to optimize the trafic lights to reach lowest waiting times possible and to secure safety and fluency of the trafic.

'''Variables'''<br />
- Propability of incoming cars from each direction<br />
- Probability of incoming pedesterians<br />
- Probability of incoming trams<br />
- Light intervals<br />

'''Situation desctription'''<br />
The crossroad is combining 3 main streets and 3 smaller streets with several pedestrians crossing and two tram crossing. There are 2 separate trafic lights to controll the traffic.

Jan Hudecek, XHUDJ17, 14/12/2015 13:50

'''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:04, 15 December 2015 (CET)

== xzimm00: Smart car traffic optimization ==

The model presented by this paper is simulating the situation of car multi-lane merging.

=== Goal ===

The primary goal of the simulation is to create a plausible real-world model of car traffic jams caused by agents operating in an inefficient way and environment that does not make the best of today's technologies that could help control the traffic flow, and to measure the possible improvement if smarter systems were employed.
The author takes into account the inefficiencies of the agents (human error...) and compares this with a (possibly better) solution using automated driver agents, always utilizing an (ideally) optimal (precomputed) traffic flow.

=== Variables ===
* Car inflow (cars/minute, distributed across the lanes)
* Number of lanes (starting and final, after the lane merge)
* Car speed (varying across the different road segments - i.e. max speed may be limited after the merge because of a traffic obstruction, for example a car accident or roadworks)
* Agents' merging strategy preference (i.e. late vs early merge)
* Agent inefficiency factor (reactions time, premature slowing down, wrong merging strategy used, varying speed, overcautiousness...)
* Amount of kept safety factor (for both human-based and automatic computer-based agent driven cars, so called 'defensive driving', expecting a failure of the others - i.e. mainly the distance kept between the cars)

=== Methods used ===
* Software used: NetLogo

=== Expected results ===
Based on the results of the simulation, it should be possible to measure the possible improvement (%) if automated car-driving agents were employed in place of humans in different critical situations of lane mergers.

(Martin Zima, xzimm00)
--[[User:Martin.zima|Martin.zima]] ([[User talk:Martin.zima|talk]]) 18:31, 14 December 2015 (CET)

'''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:06, 15 December 2015 (CET)

== Maze Solving Robot Simulation ==
=== Assignment (xkrep33) ===
==== Goal ====
The primary goal is to create an autonomous robot, which is able to find way out of the maze. The robot will be programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that will include randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.

Secondary objective will be to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.
==== Software ====
For simulation will be used NetLogo software (2D version).
==== Autonomous robot ====
Robot will be able to move up, down, left and right (viewed from above). He should be able to find the way out without any intervention. Robot will be able to move in any environment (maze) where exists at least one posible way out.
==== Environment (maze) ====
Environment will be represented by the World in NetLogo. The World will consists of black, grey and green patches. Black patches will represents the path where robot can move. The grey patches will represent walls, robot cannot enter them and finally the green patches will be the door out of the maze.

More than one enviroment will be provided. Environments will vary to prove that the robot is able to avoid an infinite loop situation.

--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 20:46, 14 December 2015 (CET)

Sounds interesting. '''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:08, 15 December 2015 (CET)

== Simulation of Aqua park ==
=== Assignment (xjanj58) ===
==== Description ====

The purpose of this simulation will be representation of Aqua park with indoor and outdoor pool. The swimmers will have random preferences and they will go to swim in one of two pools. With the pools gradually filling with swimmers it will have negative effect on motivation of each swimmer and it will show change of their preferences. Based on weather and temperature swimmers will also change their preferences between outdoor and indoor pool. Next parameter could be water purity, with high level of contamination produced by swimmers their motivation will lower and on the contrary.

==== Goals ====
Simulation will be set for period of one calendar year and at the end we can compare, if its profitable for Aquapark to have both pools open for a whole year. It will be cheaper to run outdoor pool, but bad weather will have much higher impact on outside swimmers. Next goal will be confirmation of assumption, that in summer people prefer outdoor pool and on the contrary in winter indoor pool.

==== Used software====
NetLogo

--[[User:xjanj58|xjanj58]] ([[User talk:xjanj58|talk]]) 22:36, 14 December 2015 (CET)

Sounds good, however you will probably struggle with soft parameters like "the willingness to swim in dirty water". How do you plan to deal with it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:23, 15 December 2015 (CET)

--- How about simplification in a way, that agents will produce "dirtyness" and there will be fix line, after which pool will be so dirty that nobody wants to swim in it. Indoor pool will have regular cleaning maintaince wich will lover level of dirtiness, but outside pool will only be cleaned after crossing this limit. It will take some time, lets say day or two to clean it, and then swimmers could go in again. [[User:xjanj58|xjanj58]] ([[User talk:xjanj58|talk]]) 9:56, 16 December 2015 (CET)

:: But, again, how do you turn this into specific numbers? How you will measure it? How do you know how quickly a pool gets dirty? And mainly, how will you measure the feeling of people about the quality of water. Those soft parameters are always a pain. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:52, 16 December 2015 (CET)
::--- I was thinking that agents will have parametr called e.g. "mood", and it will be interval between 0 and 10. Then for every new swimmer in the pool will this parametr go down and when it hit 0, then this particular agent will exit the pool. For dirtines, i was thinking thet pool will have some limit, for example 100, and every fifth move of the agent in the pool will fill this parametr by 1. When it reaches 100, the pool will be dirty. [[User:xjanj58|xjanj58]] ([[User talk:xjanj58|talk]]) 20:17, 16 December 2015 (CET)

::: Yes, I understand. This is exactly what I call "an academic exercise". How do you know that 0-10 is a correct interval to measure mood? How do you know that this is a linear function? Why it should go down by 1 (and not by 3 or 5 or 0.5) and why it should be the same for everyone? The same for the quality of water. Sure, you can make your model this way. But, will it be still in touch with reality? And if not, what's the point then? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:01, 16 December 2015 (CET)

== Otakar Trunda: The Ultimatum Game ==

Agent-based simulation to analyze the socio-economical experiment known as The Ultimatum Game.

=== Game rules ===

The rules can be found at [https://en.wikipedia.org/wiki/Ultimatum_game]. During the game two players interact in order to divide certain amount of money (e.g. $1000 000) between them. At the beginning, the first player proposes the ratio of division (like $900 000 for himself and $100 000 for the other player, or any other ratio). Then the second player has two choices - he can either accept - in which case the players receive the agreed amounts of money, or he can reject - in which case no player receive any money.

=== Goals ===

There are several questions that can be studied about the Ultimatum Game. For example:

* how would people behave? (assuming the players don't know each other)
* what amount of money should the first player propose to the second? (to maximize his own profit)
* what amount of money should the second player accept?
* is it rational for the second player to reject any proposal? Why?
* if the game is played repeatedly and different players use different strategies, what strategy maximizes agents' long-run profit?
* if agents' strategies can evolve in time, can altruistic strategies emerge?
* are there evolutionary stable strategies? Which ones?

=== Method ===

In the system there will be agents representing players, each of them will play according to his strategy. The strategy of the player tells how much he will offer (should he be the first player in the game) and what is the least amount he will accept (should he be the second player in the game). In each simulation step, two agents will be selected randomly and play the game. Their score will be adjusted by the result of the game (i.e. if the second agent accepts, they will gain money according to the proposal, otherwise the score remains unchanged). Agents' successfulness is determined by their score.

After a large number of simulation steps (like 1000 000 of more), we should be able to determine how the strategy affects the final score of agents (i.e. what strategies are good).

=== Evolution of strategies ===

The system can be modified such that agents' strategies will change during the simulation to emulate biological evolution. In this case, successful agents will multiply in the system (i.e. more agents will use good strategies) and non-successful agents will die-out. During the reproduction of agents, their strategies will be slightly modified - the offspring will use a slightly different strategy then the parent. This way, the system should evolve to some kind of equilibrium where either all agents use very similar strategy (static equilibrium), or different strategies survive to compete with each other (dynamic equilibrium).

After a large number of simulation steps, we should be able to observe whether altruistic strategies emerge (strategies, that offer "fair" amounts and reject small offers) and determine the existence of stable strategies (strategies that can't be taken advantage of by "parasitic" strategies).

=== Software ===

Tailor-made in C# using Visual Studio.

[[User:Trunda|Trunda]] ([[User talk:Trunda|talk]]) 17:35, 15 December 2015 (CET)

The problem is well known. However, you will probably struggle with simulating irrationality and some other particular problems. The model will tend to be either pretty complex, almost hard to manage or too trivial. I would think twice and explain how exactly you will e.g. deal with the particular strategies.
Also, why do you want to use C# instead of a certain agent framework? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 22:39, 15 December 2015 (CET)

I guess it is a well-known problem, but it seems interesting and I don't know of any similar problem that wouldn't be already well known. Can you perhaps recommend some?
How to deal with strategies: the strategy will be a pair of real numbers, lets denote them A and B from <0,1>. A will denote how much the strategy offers (e.g. A = 0.4 means that the player will offer 40% of the total amount to the second player) and B denotes how much at least the strategy accepts (e.g. B = 0.2 means that the player will reject any offer smaller than 20% of the amount and accept offers that are 20% or larger). Then the evolution can be done by a simple genetic algorithm on these pairs, fitness of the agent would be determined by its score.
I hope this setting is not too trivial, it can be further extended by adding a "space dimension" meaning that agents will only play the game with agents that are near them and during the reproduction the offspring will also be created near the parent. In this case there should emerge localized groups of agents using the same strategy, some groups should successively expand, others diminish.
I could use NetLogo, but I think it will be easier to do in C#. For example I would need to visualize the performance of the strategies - the score based on numbers A and B (mentioned earlier). This can be done in a form of a 3D graph, which I can create in C# easily using the proper library. I will also need to compute various statistics of the agents' scores to properly interpret the results (like moments and p-values) which again can be done easily using proper C# framework. It might be able to do these in NetLogo as well, but that would take me much more time :) [[User:Trunda|Trunda]] ([[User talk:Trunda|talk]]) 16:35, 16 December 2015 (CET)
::Good. According to what you have mentioned, you will also need to deal with irrationality. How you will do that? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:56, 16 December 2015 (CET)

Assignments WS 2015/2016

2015-12-14T12:29:57Z

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2015-12-14T12:28:16Z

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2015-12-14T12:26:20Z

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2015-12-14T09:44:03Z

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2015-12-11T19:06:45Z

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2015-12-11T15:42:54Z

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