Simulace.info - User contributions [en]

Simulation of product distribution

2024-01-21T19:47:00Z

Pavl08: /* Code */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
[[File:Screenshot 2024-01-21 at 10.49.42.png |thumb|900px| Simulation overview]]
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=

[[File:Screenshot 2024-01-21 at 11.12.06.png |thumb|250px| Day and weather variables]]

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

User can decide about various variables:
*'''Number_of_customers:''' how many customers will have a package delivered during the day.
*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
*'''Day:''' the user can chose what day of the week it is.
*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.

Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

This function sets up the town. It has a colour grey, bacause London is a pretty grey.
ask patches [
set pcolor grey
]

This is set up for the delivery vehicle. It always starts the day in the storage.
create-cars 1
ask cars [
setxy storagex storagey
set size 2
set shape "car"
]

Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
create-storages 1
ask storages [
setxy storagex storagey
set size 2
set shape "pentagon"
]

Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
create-customers number_of_customers
ask customers [
setxy random-xcor random-ycor
set size 2
set shape "house"
]

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
identify-closest
make-step

fill-car

if (filled_car != 20) [deliver]
if (finish_delivery = 1) [stop]

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results and conclusion=
'''Results'''

The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.

'''Conclusion'''

This simulation successfully modeled the dynamic environment in which a transport vehicle is making deliveries in the city. The results demonstrated that external factors (weather, day of the week, time and traffic) had effects on the delivery process and the delivery was slower. The needed returns to the storage facility to fill the car again was also influenced by these factors, making the time even longer - showing that real time adjustments, optimizing the shortest way possible and schedules are significant for an effective delivery.

This is a simplified version of a real world problem, and there could be more variables added, for example availability/unavailability of customers, time required to fill the car again in the storage facility, multiple vehicles and so on.

=Code=
[[File:Seminarna praca Pavlikova.nlogo]]

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

File:Seminarna praca Pavlikova.nlogo

2024-01-21T19:46:34Z

Pavl08:

Simulation of product distribution

2024-01-21T19:43:26Z

Pavl08: /* Results and conclusion */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
[[File:Screenshot 2024-01-21 at 10.49.42.png |thumb|900px| Simulation overview]]
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=

[[File:Screenshot 2024-01-21 at 11.12.06.png |thumb|250px| Day and weather variables]]

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

User can decide about various variables:
*'''Number_of_customers:''' how many customers will have a package delivered during the day.
*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
*'''Day:''' the user can chose what day of the week it is.
*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.

Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

This function sets up the town. It has a colour grey, bacause London is a pretty grey.
ask patches [
set pcolor grey
]

This is set up for the delivery vehicle. It always starts the day in the storage.
create-cars 1
ask cars [
setxy storagex storagey
set size 2
set shape "car"
]

Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
create-storages 1
ask storages [
setxy storagex storagey
set size 2
set shape "pentagon"
]

Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
create-customers number_of_customers
ask customers [
setxy random-xcor random-ycor
set size 2
set shape "house"
]

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
identify-closest
make-step

fill-car

if (filled_car != 20) [deliver]
if (finish_delivery = 1) [stop]

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results and conclusion=
'''Results'''

The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.

'''Conclusion'''

This simulation successfully modeled the dynamic environment in which a transport vehicle is making deliveries in the city. The results demonstrated that external factors (weather, day of the week, time and traffic) had effects on the delivery process and the delivery was slower. The needed returns to the storage facility to fill the car again was also influenced by these factors, making the time even longer - showing that real time adjustments, optimizing the shortest way possible and schedules are significant for an effective delivery.

This is a simplified version of a real world problem, and there could be more variables added, for example availability/unavailability of customers, time required to fill the car again in the storage facility, multiple vehicles and so on.

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

2024-01-21T19:43:14Z

Pavl08: /* Results and conclusion */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
[[File:Screenshot 2024-01-21 at 10.49.42.png |thumb|900px| Simulation overview]]
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=

[[File:Screenshot 2024-01-21 at 11.12.06.png |thumb|250px| Day and weather variables]]

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

User can decide about various variables:
*'''Number_of_customers:''' how many customers will have a package delivered during the day.
*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
*'''Day:''' the user can chose what day of the week it is.
*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.

Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

This function sets up the town. It has a colour grey, bacause London is a pretty grey.
ask patches [
set pcolor grey
]

This is set up for the delivery vehicle. It always starts the day in the storage.
create-cars 1
ask cars [
setxy storagex storagey
set size 2
set shape "car"
]

Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
create-storages 1
ask storages [
setxy storagex storagey
set size 2
set shape "pentagon"
]

Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
create-customers number_of_customers
ask customers [
setxy random-xcor random-ycor
set size 2
set shape "house"
]

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
identify-closest
make-step

fill-car

if (filled_car != 20) [deliver]
if (finish_delivery = 1) [stop]

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results and conclusion=
'''Results'''
The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.

'''Conclusion'''
This simulation successfully modeled the dynamic environment in which a transport vehicle is making deliveries in the city. The results demonstrated that external factors (weather, day of the week, time and traffic) had effects on the delivery process and the delivery was slower. The needed returns to the storage facility to fill the car again was also influenced by these factors, making the time even longer - showing that real time adjustments, optimizing the shortest way possible and schedules are significant for an effective delivery.

This is a simplified version of a real world problem, and there could be more variables added, for example availability/unavailability of customers, time required to fill the car again in the storage facility, multiple vehicles and so on.

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

2024-01-21T19:42:43Z

Pavl08: /* Results and conclusion */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
[[File:Screenshot 2024-01-21 at 10.49.42.png |thumb|900px| Simulation overview]]
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=

[[File:Screenshot 2024-01-21 at 11.12.06.png |thumb|250px| Day and weather variables]]

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

User can decide about various variables:
*'''Number_of_customers:''' how many customers will have a package delivered during the day.
*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
*'''Day:''' the user can chose what day of the week it is.
*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.

Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

This function sets up the town. It has a colour grey, bacause London is a pretty grey.
ask patches [
set pcolor grey
]

This is set up for the delivery vehicle. It always starts the day in the storage.
create-cars 1
ask cars [
setxy storagex storagey
set size 2
set shape "car"
]

Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
create-storages 1
ask storages [
setxy storagex storagey
set size 2
set shape "pentagon"
]

Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
create-customers number_of_customers
ask customers [
setxy random-xcor random-ycor
set size 2
set shape "house"
]

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
identify-closest
make-step

fill-car

if (filled_car != 20) [deliver]
if (finish_delivery = 1) [stop]

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results and conclusion=
'''Results:''' The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.

'''Conclusion:''' This simulation successfully modeled the dynamic environment in which a transport vehicle is making deliveries in the city. The results demonstrated that external factors (weather, day of the week, time and traffic) had effects on the delivery process and the delivery was slower. The needed returns to the storage facility to fill the car again was also influenced by these factors, making the time even longer - showing that real time adjustments, optimizing the shortest way possible and schedules are significant for an effective delivery.

This is a simplified version of a real world problem, and there could be more variables added, for example availability/unavailability of customers, time required to fill the car again in the storage facility, multiple vehicles and so on.

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

2024-01-21T19:29:46Z

Pavl08: /* Results and conclusion */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
[[File:Screenshot 2024-01-21 at 10.49.42.png |thumb|900px| Simulation overview]]
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=

[[File:Screenshot 2024-01-21 at 11.12.06.png |thumb|250px| Day and weather variables]]

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

User can decide about various variables:
*'''Number_of_customers:''' how many customers will have a package delivered during the day.
*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
*'''Day:''' the user can chose what day of the week it is.
*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.

Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

This function sets up the town. It has a colour grey, bacause London is a pretty grey.
ask patches [
set pcolor grey
]

This is set up for the delivery vehicle. It always starts the day in the storage.
create-cars 1
ask cars [
setxy storagex storagey
set size 2
set shape "car"
]

Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
create-storages 1
ask storages [
setxy storagex storagey
set size 2
set shape "pentagon"
]

Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
create-customers number_of_customers
ask customers [
setxy random-xcor random-ycor
set size 2
set shape "house"
]

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
identify-closest
make-step

fill-car

if (filled_car != 20) [deliver]
if (finish_delivery = 1) [stop]

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results and conclusion=
'''Results:''' The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

2024-01-21T10:38:52Z

Pavl08:

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
[[File:Screenshot 2024-01-21 at 10.49.42.png |thumb|900px| Simulation overview]]
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=

[[File:Screenshot 2024-01-21 at 11.12.06.png |thumb|250px| Day and weather variables]]

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

User can decide about various variables:
*'''Number_of_customers:''' how many customers will have a package delivered during the day.
*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
*'''Day:''' the user can chose what day of the week it is.
*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.

Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

This function sets up the town. It has a colour grey, bacause London is a pretty grey.
ask patches [
set pcolor grey
]

This is set up for the delivery vehicle. It always starts the day in the storage.
create-cars 1
ask cars [
setxy storagex storagey
set size 2
set shape "car"
]

Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
create-storages 1
ask storages [
setxy storagex storagey
set size 2
set shape "pentagon"
]

Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
create-customers number_of_customers
ask customers [
setxy random-xcor random-ycor
set size 2
set shape "house"
]

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
identify-closest
make-step

fill-car

if (filled_car != 20) [deliver]
if (finish_delivery = 1) [stop]

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results and conclusion=

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

2024-01-21T10:38:06Z

Pavl08: /* Model */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
[[File:Screenshot 2024-01-21 at 10.49.42.png |thumb|900px| Simulation overview]]
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=

[[File:Screenshot 2024-01-21 at 11.12.06.png |thumb|250px| Day and weather variables]]

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

User can decide about various variables:
*'''Number_of_customers:''' how many customers will have a package delivered during the day.
*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
*'''Day:''' the user can chose what day of the week it is.
*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.

Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

This function sets up the town. It has a colour grey, bacause London is a pretty grey.
ask patches [
set pcolor grey
]

This is set up for the delivery vehicle. It always starts the day in the storage.
create-cars 1
ask cars [
setxy storagex storagey
set size 2
set shape "car"
]

Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
create-storages 1
ask storages [
setxy storagex storagey
set size 2
set shape "pentagon"
]

Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
create-customers number_of_customers
ask customers [
setxy random-xcor random-ycor
set size 2
set shape "house"
]

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
identify-closest
make-step

fill-car

if (filled_car != 20) [deliver]
if (finish_delivery = 1) [stop]

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results=

=Conclusion=

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

2024-01-21T10:24:48Z

File:Screenshot 2024-01-21 at 10.49.42.png

2024-01-21T09:50:05Z

Pavl08:

Simulation of product distribution

2024-01-21T09:49:07Z

Pavl08: /* Model */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=
[[File:Screenshot 2024-01-21 at 00.58.01.png |thumb|400px| Simulation - delivery vehicle in the middle, next to the storage facility.]]
The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=
The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

'''External Factors'''

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

'''End of the Simulation'''

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

=Results=

=Conclusion=

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

2024-01-21T09:48:06Z

Pavl08: /* Model */

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=
This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

=Method=
[[File:Screenshot 2024-01-21 at 00.58.01.png |thumb|400px| Simulation - delivery vehicle in the middle, next to the storage facility.]]
The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

=Model=
The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

'''Environment'''

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

'''Agents'''

In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.

'''Movement'''

The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.

'''Distribution Process'''

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers

=Results=

=Conclusion=

=Code=

=Sources=
* https://www.britannica.com/place/London/Transportation
*https://www.holiday-weather.com/london/averages/
*https://www.quora.com/How-common-are-thunderstorms-in-London
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
*https://ccl.northwestern.edu/netlogo/docs/programming.html
*https://logistiko.eu/help/goods-delivery-procedure

Simulation of product distribution

Simulation of product distribution

2024-01-20T17:05:08Z

Pavl08:

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=

=Method=

=Model=

=Results=

=Conclusion=

=Code=

Simulation of product distribution

2024-01-20T17:04:55Z

Pavl08:

Introduction

'''Title:''' Simulation of product distribution

'''Author:''' Lucia Pavlíková

'''Method:''' Agent-based model

'''Tool:''' NetLogo

=Problem definition=

=Method=

=Model=

=Results=

=Conclusion=

=Code=

Simulation of product distribution

2024-01-20T17:04:45Z

Pavl08:

WS 2023/2024

2024-01-20T17:01:44Z

Pavl08:

Semestral papers from winter term 2023/2024. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2023/2024|assignment approved]]<br>

*Simulation of pandemic spread: [[Simulation of pandemic spread]], Daniel Kopecký, [[User:Kopd05|Kopd05]] ([[User talk:Kopd05|talk]]) 22:32, 16 January 2024 (CET)
*Aquatic ecosystem simulation: [[Aquatic ecosystem simulation]], Daria Tutynina, [[User:DariaTut|DariaTut]] ([[User talk:DariaTut|talk]]) 17:17, 19 January 2024 (CET)
*Space Junk: [[Space Junk]], Adam Valtr, [[User:Adamvaltr|vala18(AdamValtr)]] ([[User talk:Adamvaltr|talk]]) 14:16, 20 January 2024 (CET)
*Simulation of product distribution: [[Simulation of product distribution]], Lucia Pavlíková, [[User:LuciaPavlikova|pavl08(LuciaPavlikova)]] ([[User talk:pavl08|talk]]) 17:59, 20 January 2024 (CET)

Simulation of product distribution

2024-01-20T17:00:57Z

Pavl08: Created page with "Introduction =Problem definition= =Method= =Model= =Results= =Conclusion= =Code="

Introduction

=Problem definition=

=Method=

=Model=

=Results=

=Conclusion=

=Code=

WS 2023/2024

2024-01-20T17:00:34Z

Pavl08:

Assignments WS 2023/2024

2023-12-23T22:27:49Z

Pavl08:

{{Ambox
| text = <div>
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.
</div>
}}

{{Ambox
| text = <div>
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
| text = <div>
Topics on gambling, cards, etc. are not welcome.
</div>
}}

{{Ambox
| type = content
| 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.
</div>
}}

{{Ambox
| text = <div>
'''Criteria for evaluation of the simulation proposal'''

The proposal must contain:
*What you will simulate
*The goal of the simulation (what you analyze - i.e. not "to simulate balloon factory", but what problem should the simulation solve).
*Who would actually use such simulation (example of such user) and how would it help him
*What method and simulation environment you plan to use. Choose only from the development environments that we have used in the course.
*What variables will be incorporated
*What variables will be random
*What exact data you will base values of your variables on
*(In case of Monte Carlo) What exact data you will base your determination of probability distribution of your random variables on
*What exact data you will base your formulas in the simulation (simulation behavior) on

'''If any of the above points are missing from the simulation proposal, the proposal is considered incomplete. Unless the proposal contains all of the above points it will not be evaluated at all (and therefore cannot be approved).'''

# Is it clear from the proposed assignment how the simulation will work?
# Does the simulation make sense?
# Is the simulation model complex enough to simulate credibly the real-world phenomenon that the simulation tries to simulate?
# Is the data used real and relevant for the real-world phenomenon that the simulation tries to simulate?
# Is the simulation feasible? (in given time by the course)

'''If the answer to any of the above points is no, you need to improve your proposal. Don't wait for us to tell you so - you're wasting your time.'''

</div>
}}

== Kopd05 - Simulation of pandemic spread ==

*The aim of the simulation will be to investigate the spread of different viruses, according to their coefficient of spread, recovery or mortality.
*The simulation can be used by epidemiologists to study the spread of viruses and predict the evolution of a pandemic.
*The simulation will be developed using the NetLogo agent-based model
*The following variables will be included in the simulation:
**Virus spread rate
**Chance of recovery of the individual
**Risk of death
**Number of individuals
**Number of people infected
**% of immune individuals (random)
**Vaccination rate
**isImmune
**isInfected
**isVaccinated
**ChanceofCure (random)

* Data can be based on real virus data or can be set individually. <br>For example, the spread of a virus will be based on the reproduction number of R (can be looked up on the internet)

* Everything will be based on freely available online data related to the topic, virus, etc.

[[User:Kopd05|Kopd05]] ([[User talk:Kopd05|talk]]) 21:02, 12 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]])
:: Very general. Please, specify in deep. What kind(s) of viruses, how will you model the population, how should it look like, etc. etc. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:15, 20 December 2023 (CET)

== Tutd00 - Aquatic ecosystem simulation ==

*The simulation is inspired by the classic "predator and prey" model. The goal is to model the behavior of fish, plants and predators in the generated aquatic environment.
*The simulation will be developed using the NetLogo - Agent based model
*The following variables will be in the simulation:
** The water temperature
** Pollution level
** Number of fish
** Number of sharks
** Number of plants
** Time of death of fish and shark (random)
** Fish and shark breeding time (random, but only after feeding)
** and also variables that depend on the basic ones: number of dead fish and sharks, number of plants eaten

* The user has the option to set the number of fish, plants, sharks, water temperature and pollution level when starting the simulation.
* The rules: Cold water temperatures accelerate the rate of reproduction of predators, while warm water speeds up the rate of reproduction of fish.High water pollution slows down the reproduction of fish and sharks, but speeds up the growth of plants. The user can change settings during the simulation.
* This simulation allows the user to investigate how different parameter configurations affect ecosystem stability and dynamics and could be modified and used to model real aquatic systems.
* The data used in the simulation is based on real sources dedicated to the topic of aquatic systems (example: https://www.nalms.org/wp-content/uploads/2018/09/31-2-5.pdf), but will be implemented in a simplified form.

[[User:DariaTut|DariaTut]] ([[User talk:DariaTut|talk]]) 10:47, 18 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:58, 19 December 2023 (CET)
:'''Edited''' [[User:DariaTut|DariaTut]] ([[User talk:DariaTut|talk]]) 20:27, 19 December 2023 (CET)
:: Predator-prey is one of the most widely implemented models. Hence, you should suggest something what make your implementation special and new. What is the added value compared to the predator-prey? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:19, 20 December 2023 (CET)

:: The simulation uniquely models a predator-prey scenario in a water-based ecosystem, incorporating both agent interactions and environmental conditions like water temperature and pollution. I have never seen a simulation on this topic anywhere.
:: '''To make the simulation more unique, I will add three types of fish and three types of plants, with each fish having its preferences in plants. The fish that prefers a wider range of plants will have better survival chances. This will allow users to explore and fine-tune the ideal balance for maximizing ecological diversity and understand how to sustain it.''' [[User:DariaTut|DariaTut]] ([[User talk:DariaTut|talk]]) 19:39, 20 December 2023 (CET)
::: All right. Please, stick to the real data. '''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 20:31, 20 December 2023 (CET)

== Kubs09 - Simulation of Passenger Behavior at the Main Train Station ==

*Topic: Passenger behavior when boarding trains at the entire Main Train Station
*Utilization: This could be utilized, for instance, by Czech Railways/main station administrators to better adjust trains and their arrival positions.
*Method: Agent-based modelling, Netlogo
*Variables:
**Number of passengers
**Number of trains
**Passengers positions
**Timetable (train departures/arrivals)
**Delays (random)
**isCollision
**isDelay(random)

* Sources: For this simulation, predominantly sources from Google Scholar would be used, or scientific articles found in the E-library of VSE.

[[User:Kubs09|Kubs09]] ([[User talk:Kubs09|talk]]) 20:35, 19 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:58, 19 December 2023 (CET)
:'''Edited.'''
:: This could be a good simulation. I would expect you will try to obtain data from Czech Railways maily, etc. How the simulation should look like specifically? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:21, 20 December 2023 (CET)
'''I would try to obtain some data from Czech railways if possible. I was thinking of modelling the train platforms on main station (1-7), would research for the positions of the trains in the station and how people react to different train placements or if they prefer to move into different wagons if the one chosen is full.''' [[User:Kubs09|Kubs09]] ([[User talk:Kubs09|talk]]) 21:35, 20 December 2023 (CET)
:OK, to be honest, I perhaps don't understand it well. What is the purpose of the simulation? You will simulate the positions of trains for what? In order to optimize how people get to the trains? It is still not clear to me. I even don't see any citations that usually help me to get familiar with the problem, but you don't have any. Please, elaborate on it. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 23:19, 20 December 2023 (CET)

== Vysj06 - Simulation of Agricultural Production and Climate Change ==

*Objective of the simulation: To model the impact of climate change on agricultural production, including soil fertility, crop yields, and irrigation.
*Usage: To provide farmers, scientists, and policymakers with tools for better planning and adaptation to climate changes.
*Method: Agent-based modeling, using NetLogo.
*Variables:
**Types of crops (grains, vegetables, fruits).
**Soil conditions and their changes.
**Amount and distribution of precipitation.
**Temperature changes.
**Irrigation methods and their efficiency.

*Data: Based on real climate and agricultural data, including historical trends and forecasts. Option to configure parameters.
*Output: The simulation will provide users with the ability to visualize and understand the impact of various climate scenarios on agricultural production and possible adaptation strategies.

[[User:Vysj06|Vysj06]] ([[User talk:Vysj06|talk]]) 21:25, 19 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:59, 19 December 2023 (CET)
:'''Edited'''
:: This could be a good model, but, please, specify it deeper. How it should look like? What questions specifically will you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:22, 20 December 2023 (CET)

:'''Edit:''' The model will consist of fields that contain crops and data about the soil condition. Over time, the climate (temperature, precipitation) will change based on the data input at the beginning of the simulation, and this will have a certain impact on the yields and soil quality for further cultivation. Thus, it will simulate the development of farming possibilities on the fields according to climate change. It will answer questions about how significant an impact a change in precipitation or temperature over several years can have on the cultivation of various crops. [[User:Vysj06|Vysj06]] ([[User talk:Vysj06|talk]]) 20:32, 20 December 2023 (CET)
::: I understand it. But did you do a preliminary research? How you will e.g. measure the soil quality? What types of adaptation strategies do you plan to test? Etc. etc. Be specific, please. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 20:49, 20 December 2023 (CET)
:'''Edit:''' I did some research. To measure soil quality I can use indicators like moisture, pH, nutrients, and organic matter, as discussed in study "Climate Change and Agriculture: A Review of Impacts and Adaptations". This study also highlights the importance of adaptations such as developing more resilient crop varieties and optimizing irrigation systems in response to climate changes. Additional relevant information on the impacts of climate change on soil health and its chemical and biological properties is discussed in the study "Impact of Climate Change on Soil Health" from Science International, emphasizing changes in microbial activity, acidity, and nutrient availability in soil.
*https://openknowledge.worldbank.org/entities/publication/a4373f71-f7b9-57c3-a66d-cf3f6a121c73
*https://scialert.net/fulltext/?doi=sciintl.2016.51.73
[[User:Vysj06|Vysj06]] ([[User talk:Vysj06|talk]]) 21:30, 20 December 2023 (CET)
:I am still not entirely convinced this topic is specific enough. It could happen that you will have a nice, working model, but providing completely useless results, what would have a serious impact on the evaluation. The solution of the problem is critical, not the model. But if you will be able to provide meaningful outputs, it is fine. '''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 23:07, 20 December 2023 (CET)

== Doba00 - Factors that influence beer fermentation ==

<div>
*Simulation: This simulation will focus on addressing external and internal factors that have an effect on beer fermentation and influence alcohol percentage in the final product.
** Can be used by brewery makers in the food industry to better evaluate beer-making conditions in order to make preferred outcomes of alcohol levels.
*Incorporated variables:
**temperature,
**yeast type,
**gravity of the wort,
**carbon dioxide production,
**cooling,
**yeast reuse

*Goal: The simulation aims to analyze factors affecting the beer fermentation process and evaluate the best conditions for getting preferred outcomes in alcohol percentage in the final product.

*Method: Vensim

*Data:
**https://www.phind.com/search?cache=uu8fcm0sqdc1fyznvi0o0rhx&fbclid=IwAR30Y-m8maeJI3WFcW-fLvqTlRov_63mfnCGPxC9RiANZQbkQaYNO8MDl0M
**https://www.researchgate.net/publication/284725490_Biochemistry_of_beer_fermentation
**chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://ucitelchemie.upol.cz/materialy/vkpch/vyroba_piva_text_pro_ucitele.pdf
**https://grainfather.com/beer-fermentation-process/#:~:text=Typically%20the%20cooler%20the%20temperature,unwanted%20attributes%20in%20the%20beer.

*Author: [[User:Doba00|Doba00]] ([[User talk:Doba00|talk]]) 16:25, 19 December 2023 (CET)
</div>
::Please provide us with the reference to particular data, you wil base your simulation on. How exactly will your simulation work? How will you simulate the dynamics of food prices in the food industry? From what data you will derive the formulas neccesary for it? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 17:17, 19 December 2023 (CET)
:: '''Edited'''. After a thorough consideration, I have decided to change the topic completely. I hope it is more suitable now. [[User:Doba00|Doba00]] ([[User talk:Doba00|talk]]) 15:51, 20 December 2023 (CET)
:::: '''Approved'''[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:15, 20 December 2023 (CET)

== Tata05 - Simulation of the Ocean Carbon Uptake and Atmospheric Carbon Dioxide ==

<div>
*Problem definition: I want to simulate the process of the Life cycle of processing carbon dioxide from the atmosphere and increasing the stored carbon dioxide on the ocean floor. This process influences ocean acidification and affects the entire climate. The ocean absorbs carbon dioxide from the atmosphere wherever air meets water. Regarding scientists oceans absorb 30% of our emissions, driven by a huge carbon pump.
*Method: Agent-based simulation, NetLogo.
*Variabels:
**Solar Energy
**Atmospheric CO2
**Changes in temperature
**Change in water acidity
**CO2 dissolving
**Carbon capture and storage
*Resource: Information from National Oceanic and Atmospheric Administration, Nasa Global Climate, https://www.soest.hawaii.edu/oceanography/faculty/zeebe_files/Publications/ZeebeWolfEnclp07.pdf

[[User:Tata05| Tata05]] ([[User talk:Tata05|talk]]) 16:46, 19 December 2023 (CET)
::Please provide us with the reference to literature with formulas you will base your simulation on. Without it, it is impossible to evaluate wheather the simulation will make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 17:13, 19 December 2023 (CET)

::Reference: https://www.soest.hawaii.edu/oceanography/faculty/zeebe_files/Publications/ZeebeWolfEnclp07.pdf
:'''Edited''' [[User:Tata05|Tata05]] ([[User talk:Tata05|Tata05]]) 21:49, 19 December 2023 (CET)
:: My opinion is that this could be rather a good model for systems dynamics. What do you think, Oleg? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:25, 20 December 2023 (CET)
::: Agreed. For Vensim '''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:16, 20 December 2023 (CET)

==akee00== Urban Traffic flow and Pollution Control in Prague ==

<div>
*Primary objective: To analyze the impact of different traffic management strategies on urban traffic flow and air pollution levels in Prague.
*Problem to solve: Determining the most effective traffic management strategy that minimizes traffic congestion and reduces air pollution in the city of Prague.
*Context: With growing urban populations, traffic congestion and pollution have become critical issues. This simulation aims to explore how various traffic control measures can alleviate these problems.
*Method and Simulation Environment:
**Agent based Modelling
**Simulation Tool: Netlogo.
*Environment Setup: A simulated urban area with a grid of streets, traffic signals, vehicles, and pollution indicators.
*Variables and Data:
*Random variables:
**Vehicle breakdowns,
**Driver behaviour: route choice and speed variability
**Traffic incidents
**Weather conditions
**Vehicle emission rates.
*Incorporated (deterministic) variables:
**Vehicle agents: count, types
**Traffic signal agents: signal timing, adaptive signals
**Pollution measurement: Baseline emission levels, Air quality index
**Traffic Management Strategies
**Road layout.
*Data source:
**Traffic and transportation data for Prague from praha.eu
*Expected outcome: The simulation should reveal the most effective traffic management strategies for reducing congestion and pollution. By comparing these results with real-world data, urban planners can make informed decisions to improve traffic flow and air quality in Prague.

: This is generally a

: This is generally a good topic, however the scope you suggest is really large and you would be hardly able to deliver results. Limit the model reasonably, e.g. choose just a limited area or limit the model different way. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 20:04, 19 December 2023 (CET)

== vala18 - Space Junk: a satelite/debris collision simulator ==

* I would like to simulate a movement of satellites and space debris (AKA space junk) in a subsection of earths orbit (represented as 2D plane), where the satellites move on predetermined trajectories, but the junk's motion is somewhat randomized. If debris collides with another debris, it changes direction and creates little bit more debris, if debris collides with a satelite, the satelite is destroyed and a lot of debris is created. Then I would run the simulation to calculate a meantime between collisions based on starting conditions, such as the number of satellites, debris density and debris multiplication (how many pieces of additional debris collisions create).
* Space debris poses a significant issue due to the growing amount of defunct satellites, spent rocket stages, and fragments in Earth's orbit. The escalating debris increases collision risks, jeopardizing operational satellites and future space missions. This problem threatens space infrastructure, exacerbates space congestion, and raises the specter of generating even more fragments through collisions, potentially creating a self-sustaining cycle of space debris proliferation. Addressing this issue is crucial to ensuring the sustainability of space activities and preventing long-term consequences for Earth's orbital environment and space exploration. Proposed simulator could help two show and calculate conditions uder which such a self-sustaining debris proliferation arises; albeit with a simplified model, and hopefully educate about the aforementioned issue.
* NetLogo - agent based modeling (two types of agents: satellites and debris)
** The model will make the following simplifications:
*** subsection of Earth's orbit will be represented as a 2D "map" of fixed size (pixels) forming a grid
*** objects (satellites and debris) can move on the map only in 8 distinct directions
*** all objects move at the same speed (one pixel per turn)
*** objects are squares; the minimal size is 1x1 pixel, then 2x2 pixels, etc...
*** when objects collide the resulting number of debris is a random integer between 1 and the sum of sizes (pixels) of colliding objects.
*** mass of objects is proportional to their size, i.e. 1 (pixel) = 1 unit of mass
*** all objects follow Newton's laws of motion
* Variables for starting conditions
** map size
** number of satellites
** number of debris
** starting position and direction of objects
* Variables of agents (satellites and debris)
** size
** mass
** direction
* In order to simulate realistic scenarios - Data from NASA and other relevant studies regarding density of debris and number of satellites in orbit will be obtained to inform the starting conditions of the proposed model. Namely, I plan to use data obtained and published by:
**[https://orbitaldebris.jsc.nasa.gov/|NASA ORBITAL DEBRIS PROGRAM OFFICE]
**[https://platform.leolabs.space/visualization|LEOLABS Low Earth Orbit Visualization]

[[User:Adamvaltr|vala18(AdamValtr)]] ([[User talk:Adamvaltr|talk]]) 16:57, 20 December 2023 (CET)
: '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 20 December 2023 (CET)

== pavl08 - Climate Change Simulation ==
* I would like to simulate climate change in a certain area - how it affected the nature, plant and animal species, temperature and the people living in the area
**the simulation will demonstrate, what can happen to a certain area of our Earth if we do or don’t take action and slow down the climate change
**the simulation will be developed in NetLogo - agent based model
**variables:
***temperature
***the year
***level of "planet destruction"
***number of people
***number of trees
***number of animal species
***(there might be other variables added based on the development of the simulation)
**the year and planet destruction level will be chosen by the player
**the number of people will be calculated from the year the player chooses and the “planet destruction” parameter - if the parameter is moderate, the number of people will be high, if the “planet destruction” is low or high, there will be a lot of people
**temperature can affect the number of animal species, number of trees will be random
**the simulation will try to demonstrate what will happen to our planet in future based on our decisions now
**it can be used as a tool for informing people about the issue and the consequences of our actions to the future
**the sources for data will be used these for example: https://www.unesco.org/en/climate-change/education, https://www.ipcc.ch/reports/, https://climate.nasa.gov/
:Very general and too complex. Systems dynamic is much better for this problem. You should define some particular problem, much more detailed. Mention specific resources, papers, not general links. I suggest to find something different. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 23:12, 20 December 2023 (CET)

I thought about something different - '''Simulation of product distribution'''
*the simulation will be about how products are distributed in London for one company (I chose London because I was able to find data about its transport and weather)
*it could help new companies trying to figure out how to most effectively distribute products between either storage facilities or their stores in London - however it can be later used in other cities as well
*the simulation will be developed in NetLogo - agent based model
*variables:
**number of cars/trucks
**storage facilities, stores, company
**transport nodes
**time of delivery, expenses
**external factors (weather) - will be random
*variables such as number of cars/trucks, number of storage facilities and stores will be chosen by the user
*data about transport in London: https://tfl.gov.uk/corporate/publications-and-reports/travel-in-london-reports, https://tfl.gov.uk/info-for/open-data-users/our-open-data,
*data about weather in UK: https://www.metoffice.gov.uk/research/cli
mate/maps-and-data/historic-station-data

Assignments WS 2023/2024

2023-12-20T20:38:57Z

Pavl08:

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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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Topics on gambling, cards, etc. are not welcome.
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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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'''Criteria for evaluation of the simulation proposal'''

The proposal must contain:
*What you will simulate
*The goal of the simulation (what you analyze - i.e. not "to simulate balloon factory", but what problem should the simulation solve).
*Who would actually use such simulation (example of such user) and how would it help him
*What method and simulation environment you plan to use. Choose only from the development environments that we have used in the course.
*What variables will be incorporated
*What variables will be random
*What exact data you will base values of your variables on
*(In case of Monte Carlo) What exact data you will base your determination of probability distribution of your random variables on
*What exact data you will base your formulas in the simulation (simulation behavior) on

'''If any of the above points are missing from the simulation proposal, the proposal is considered incomplete. Unless the proposal contains all of the above points it will not be evaluated at all (and therefore cannot be approved).'''

# Is it clear from the proposed assignment how the simulation will work?
# Does the simulation make sense?
# Is the simulation model complex enough to simulate credibly the real-world phenomenon that the simulation tries to simulate?
# Is the data used real and relevant for the real-world phenomenon that the simulation tries to simulate?
# Is the simulation feasible? (in given time by the course)

'''If the answer to any of the above points is no, you need to improve your proposal. Don't wait for us to tell you so - you're wasting your time.'''

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== Kopd05 - Simulation of pandemic spread ==

*The aim of the simulation will be to investigate the spread of different viruses, according to their coefficient of spread, recovery or mortality.
*The simulation can be used by epidemiologists to study the spread of viruses and predict the evolution of a pandemic.
*The simulation will be developed using the NetLogo agent-based model
*The following variables will be included in the simulation:
**Virus spread rate
**Chance of recovery of the individual
**Risk of death
**Number of individuals
**Number of people infected
**% of immune individuals (random)
**Vaccination rate
**isImmune
**isInfected
**isVaccinated
**ChanceofCure (random)

* Data can be based on real virus data or can be set individually. <br>For example, the spread of a virus will be based on the reproduction number of R (can be looked up on the internet)

* Everything will be based on freely available online data related to the topic, virus, etc.

[[User:Kopd05|Kopd05]] ([[User talk:Kopd05|talk]]) 21:02, 12 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]])
:: Very general. Please, specify in deep. What kind(s) of viruses, how will you model the population, how should it look like, etc. etc. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:15, 20 December 2023 (CET)

== Tutd00 - Aquatic ecosystem simulation ==

*The simulation is inspired by the classic "predator and prey" model. The goal is to model the behavior of fish, plants and predators in the generated aquatic environment.
*The simulation will be developed using the NetLogo - Agent based model
*The following variables will be in the simulation:
** The water temperature
** Pollution level
** Number of fish
** Number of sharks
** Number of plants
** Time of death of fish and shark (random)
** Fish and shark breeding time (random, but only after feeding)
** and also variables that depend on the basic ones: number of dead fish and sharks, number of plants eaten

* The user has the option to set the number of fish, plants, sharks, water temperature and pollution level when starting the simulation.
* The rules: Cold water temperatures accelerate the rate of reproduction of predators, while warm water speeds up the rate of reproduction of fish.High water pollution slows down the reproduction of fish and sharks, but speeds up the growth of plants. The user can change settings during the simulation.
* This simulation allows the user to investigate how different parameter configurations affect ecosystem stability and dynamics and could be modified and used to model real aquatic systems.
* The data used in the simulation is based on real sources dedicated to the topic of aquatic systems (example: https://www.nalms.org/wp-content/uploads/2018/09/31-2-5.pdf), but will be implemented in a simplified form.

[[User:DariaTut|DariaTut]] ([[User talk:DariaTut|talk]]) 10:47, 18 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:58, 19 December 2023 (CET)
:'''Edited''' [[User:DariaTut|DariaTut]] ([[User talk:DariaTut|talk]]) 20:27, 19 December 2023 (CET)
:: Predator-prey is one of the most widely implemented models. Hence, you should suggest something what make your implementation special and new. What is the added value compared to the predator-prey? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:19, 20 December 2023 (CET)

:: The simulation uniquely models a predator-prey scenario in a water-based ecosystem, incorporating both agent interactions and environmental conditions like water temperature and pollution. I have never seen a simulation on this topic anywhere.
:: '''To make the simulation more unique, I will add three types of fish and three types of plants, with each fish having its preferences in plants. The fish that prefers a wider range of plants will have better survival chances. This will allow users to explore and fine-tune the ideal balance for maximizing ecological diversity and understand how to sustain it.''' [[User:DariaTut|DariaTut]] ([[User talk:DariaTut|talk]]) 19:39, 20 December 2023 (CET)
::: All right. Please, stick to the real data. '''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 20:31, 20 December 2023 (CET)

== Kubs09 - Simulation of Passenger Behavior at the Main Train Station ==

*Topic: Passenger behavior when boarding trains at the entire Main Train Station
*Utilization: This could be utilized, for instance, by Czech Railways/main station administrators to better adjust trains and their arrival positions.
*Method: Agent-based modelling, Netlogo
*Variables:
**Number of passengers
**Number of trains
**Passengers positions
**Timetable (train departures/arrivals)
**Delays (random)
**isCollision
**isDelay(random)

* Sources: For this simulation, predominantly sources from Google Scholar would be used, or scientific articles found in the E-library of VSE.

[[User:Kubs09|Kubs09]] ([[User talk:Kubs09|talk]]) 20:35, 19 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:58, 19 December 2023 (CET)
:'''Edited.'''
:: This could be a good simulation. I would expect you will try obtain data from Czech Railways maily, etc. How the simulation should look like specifically? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:21, 20 December 2023 (CET)
'''I would try to obtain some data from Czech railways if possible. I was thinking of modelling the train platforms on main station (1-7), would research for the positions of the trains in the station and how people react to different train placements or if they prefer to move into different wagons if the one chosen is full.''' [[User:Kubs09|Kubs09]] ([[User talk:Kubs09|talk]]) 21:35, 20 December 2023 (CET)

== Vysj06 - Simulation of Agricultural Production and Climate Change ==

*Objective of the simulation: To model the impact of climate change on agricultural production, including soil fertility, crop yields, and irrigation.
*Usage: To provide farmers, scientists, and policymakers with tools for better planning and adaptation to climate changes.
*Method: Agent-based modeling, using NetLogo.
*Variables:
**Types of crops (grains, vegetables, fruits).
**Soil conditions and their changes.
**Amount and distribution of precipitation.
**Temperature changes.
**Irrigation methods and their efficiency.

*Data: Based on real climate and agricultural data, including historical trends and forecasts. Option to configure parameters.
*Output: The simulation will provide users with the ability to visualize and understand the impact of various climate scenarios on agricultural production and possible adaptation strategies.

[[User:Vysj06|Vysj06]] ([[User talk:Vysj06|talk]]) 21:25, 19 December 2023 (CET)

:'''This course is in English. We accept English versions only.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:59, 19 December 2023 (CET)
:'''Edited'''
:: This could be a good model, but, please, specify it deeper. How it should look like? What questions specifically will you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:22, 20 December 2023 (CET)

:'''Edit:''' The model will consist of fields that contain crops and data about the soil condition. Over time, the climate (temperature, precipitation) will change based on the data input at the beginning of the simulation, and this will have a certain impact on the yields and soil quality for further cultivation. Thus, it will simulate the development of farming possibilities on the fields according to climate change. It will answer questions about how significant an impact a change in precipitation or temperature over several years can have on the cultivation of various crops. [[User:Vysj06|Vysj06]] ([[User talk:Vysj06|talk]]) 20:32, 20 December 2023 (CET)
::: I understand it. But did you do a preliminary research? How you will e.g. measure the soil quality? What types of adaptation strategies do you plan to test? Etc. etc. Be specific, please. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 20:49, 20 December 2023 (CET)
:'''Edit:''' I did some research. To measure soil quality I can use indicators like moisture, pH, nutrients, and organic matter, as discussed in study "Climate Change and Agriculture: A Review of Impacts and Adaptations". This study also highlights the importance of adaptations such as developing more resilient crop varieties and optimizing irrigation systems in response to climate changes. Additional relevant information on the impacts of climate change on soil health and its chemical and biological properties is discussed in the study "Impact of Climate Change on Soil Health" from Science International, emphasizing changes in microbial activity, acidity, and nutrient availability in soil.
*https://openknowledge.worldbank.org/entities/publication/a4373f71-f7b9-57c3-a66d-cf3f6a121c73
*https://scialert.net/fulltext/?doi=sciintl.2016.51.73
[[User:Vysj06|Vysj06]] ([[User talk:Vysj06|talk]]) 21:30, 20 December 2023 (CET)

== Doba00 - Factors that influence beer fermentation ==

<div>
*Simulation: This simulation will focus on addressing external and internal factors that have an effect on beer fermentation and influence alcohol percentage in the final product.
** Can be used by brewery makers in the food industry to better evaluate beer-making conditions in order to make preferred outcomes of alcohol levels.
*Incorporated variables:
**temperature,
**yeast type,
**gravity of the wort,
**carbon dioxide production,
**cooling,
**yeast reuse

*Goal: The simulation aims to analyze factors affecting the beer fermentation process and evaluate the best conditions for getting preferred outcomes in alcohol percentage in the final product.

*Method: Vensim

*Data:
**https://www.phind.com/search?cache=uu8fcm0sqdc1fyznvi0o0rhx&fbclid=IwAR30Y-m8maeJI3WFcW-fLvqTlRov_63mfnCGPxC9RiANZQbkQaYNO8MDl0M
**https://www.researchgate.net/publication/284725490_Biochemistry_of_beer_fermentation
**chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://ucitelchemie.upol.cz/materialy/vkpch/vyroba_piva_text_pro_ucitele.pdf
**https://grainfather.com/beer-fermentation-process/#:~:text=Typically%20the%20cooler%20the%20temperature,unwanted%20attributes%20in%20the%20beer.

*Author: [[User:Doba00|Doba00]] ([[User talk:Doba00|talk]]) 16:25, 19 December 2023 (CET)
</div>
::Please provide us with the reference to particular data, you wil base your simulation on. How exactly will your simulation work? How will you simulate the dynamics of food prices in the food industry? From what data you will derive the formulas neccesary for it? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 17:17, 19 December 2023 (CET)
:: '''Edited'''. After a thorough consideration, I have decided to change the topic completely. I hope it is more suitable now. [[User:Doba00|Doba00]] ([[User talk:Doba00|talk]]) 15:51, 20 December 2023 (CET)
:::: '''Approved'''[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:15, 20 December 2023 (CET)

== Tata05 - Simulation of the Ocean Carbon Uptake and Atmospheric Carbon Dioxide ==

<div>
*Problem definition: I want to simulate the process of the Life cycle of processing carbon dioxide from the atmosphere and increasing the stored carbon dioxide on the ocean floor. This process influences ocean acidification and affects the entire climate. The ocean absorbs carbon dioxide from the atmosphere wherever air meets water. Regarding scientists oceans absorb 30% of our emissions, driven by a huge carbon pump.
*Method: Agent-based simulation, NetLogo.
*Variabels:
**Solar Energy
**Atmospheric CO2
**Changes in temperature
**Change in water acidity
**CO2 dissolving
**Carbon capture and storage
*Resource: Information from National Oceanic and Atmospheric Administration, Nasa Global Climate, https://www.soest.hawaii.edu/oceanography/faculty/zeebe_files/Publications/ZeebeWolfEnclp07.pdf

[[User:Tata05| Tata05]] ([[User talk:Tata05|talk]]) 16:46, 19 December 2023 (CET)
::Please provide us with the reference to literature with formulas you will base your simulation on. Without it, it is impossible to evaluate wheather the simulation will make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 17:13, 19 December 2023 (CET)

::Reference: https://www.soest.hawaii.edu/oceanography/faculty/zeebe_files/Publications/ZeebeWolfEnclp07.pdf
:'''Edited''' [[User:Tata05|Tata05]] ([[User talk:Tata05|Tata05]]) 21:49, 19 December 2023 (CET)
:: My opinion is that this could be rather a good model for systems dynamics. What do you think, Oleg? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:25, 20 December 2023 (CET)
::: Agreed. For Vensim '''Approved''' [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 20:16, 20 December 2023 (CET)

==akee00== Urban Traffic flow and Pollution Control in Prague ==

<div>
*Primary objective: To analyze the impact of different traffic management strategies on urban traffic flow and air pollution levels in Prague.
*Problem to solve: Determining the most effective traffic management strategy that minimizes traffic congestion and reduces air pollution in the city of Prague.
*Context: With growing urban populations, traffic congestion and pollution have become critical issues. This simulation aims to explore how various traffic control measures can alleviate these problems.
*Method and Simulation Environment:
**Agent based Modelling
**Simulation Tool: Netlogo.
*Environment Setup: A simulated urban area with a grid of streets, traffic signals, vehicles, and pollution indicators.
*Variables and Data:
*Random variables:
**Vehicle breakdowns,
**Driver behaviour: route choice and speed variability
**Traffic incidents
**Weather conditions
**Vehicle emission rates.
*Incorporated (deterministic) variables:
**Vehicle agents: count, types
**Traffic signal agents: signal timing, adaptive signals
**Pollution measurement: Baseline emission levels, Air quality index
**Traffic Management Strategies
**Road layout.
*Data source:
**Traffic and transportation data for Prague from praha.eu
*Expected outcome: The simulation should reveal the most effective traffic management strategies for reducing congestion and pollution. By comparing these results with real-world data, urban planners can make informed decisions to improve traffic flow and air quality in Prague.

: This is generally a

: This is generally a good topic, however the scope you suggest is really large and you would be hardly able to deliver results. Limit the model reasonably, e.g. choose just a limited area or limit the model different way. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 20:04, 19 December 2023 (CET)

== vala18 - Space Junk: a satelite/debris collision simulator ==

* I would like to simulate a movement of satellites and space debris (AKA space junk) in a subsection of earths orbit (represented as 2D plane), where the satellites move on predetermined trajectories, but the junk's motion is somewhat randomized. If debris collides with another debris, it changes direction and creates little bit more debris, if debris collides with a satelite, the satelite is destroyed and a lot of debris is created. Then I would run the simulation to calculate a meantime between collisions based on starting conditions, such as the number of satellites, debris density and debris multiplication (how many pieces of additional debris collisions create).
* Space debris poses a significant issue due to the growing amount of defunct satellites, spent rocket stages, and fragments in Earth's orbit. The escalating debris increases collision risks, jeopardizing operational satellites and future space missions. This problem threatens space infrastructure, exacerbates space congestion, and raises the specter of generating even more fragments through collisions, potentially creating a self-sustaining cycle of space debris proliferation. Addressing this issue is crucial to ensuring the sustainability of space activities and preventing long-term consequences for Earth's orbital environment and space exploration. Proposed simulator could help two show and calculate conditions uder which such a self-sustaining debris proliferation arises; albeit with a simplified model, and hopefully educate about the aforementioned issue.
* NetLogo - agent based modeling (two types of agents: satellites and debris)
** The model will make the following simplifications:
*** subsection of Earth's orbit will be represented as a 2D "map" of fixed size (pixels) forming a grid
*** objects (satellites and debris) can move on the map only in 8 distinct directions
*** all objects move at the same speed (one pixel per turn)
*** objects are squares; the minimal size is 1x1 pixel, then 2x2 pixels, etc...
*** when objects collide the resulting number of debris is a random integer between 1 and the sum of sizes (pixels) of colliding objects.
*** mass of objects is proportional to their size, i.e. 1 (pixel) = 1 unit of mass
*** all objects follow Newton's laws of motion
* Variables for starting conditions
** map size
** number of satellites
** number of debris
** starting position and direction of objects
* Variables of agents (satellites and debris)
** size
** mass
** direction
* In order to simulate realistic scenarios - Data from NASA and other relevant studies regarding density of debris and number of satellites in orbit will be obtained to inform the starting conditions of the proposed model. Namely, I plan to use data obtained and published by:
**[https://orbitaldebris.jsc.nasa.gov/|NASA ORBITAL DEBRIS PROGRAM OFFICE]
**[https://platform.leolabs.space/visualization|LEOLABS Low Earth Orbit Visualization]

[[User:Adamvaltr|vala18(AdamValtr)]] ([[User talk:Adamvaltr|talk]]) 16:57, 20 December 2023 (CET)
: '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 20 December 2023 (CET)

== pavl08 - Climate Change Simulation ==
* I would like to simulate climate change in a certain area - how it affected the nature, plant and animal species, temperature and the people living in the area
**the simulation will demonstrate, what can happen to a certain area of our Earth if we do or don’t take action and slow down the climate change
**the simulation will be developed in NetLogo - agent based model
**variables:
***temperature
***the year
***level of "planet destruction"
***number of people
***number of trees
***number of animal species
***(there might be other variables added based on the development of the simulation)
**the year and planet destruction level will be chosen by the player
**the number of people will be calculated from the year the player chooses and the “planet destruction” parameter - if the parameter is moderate, the number of people will be high, if the “planet destruction” is low or high, there will be a lot of people
**temperature can affect the number of animal species, number of trees will be random
**the simulation will try to demonstrate what will happen to our planet in future based on our decisions now
**it can be used as a tool for informing people about the issue and the consequences of our actions to the future
**the sources for data will be used these for example: https://www.unesco.org/en/climate-change/education, https://www.ipcc.ch/reports/, https://climate.nasa.gov/