Assignments WS 2021/2022
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Spread of covid19 in closed/open area markets
In winter 2021 in Czechia the christmas markets were banned due to another covid19 infection wave. On the other hand people are free to go into shopping malls. It would be interesting to use existing data about covid19 virus transmission in agent based simulation to see how many people get infected and in what speed depending on whether they are in a christmas (open) market, or in a shopping mall (closed). The main goal will be to see if the simulation would backup the decision that has been made about christmas markets.
Possible research papers that contain data about covid spreading
- Kaggle notebook - Covid19, Evolution, Transmission, Spatial Patterns
- Research - Understanding COVID-19 transmission, health impacts and mitigation: timely social distancing is the key
This simulation would be realised using NetLogo.
WHAT will be simulated
- market place, which can be both open space or closed space.
- people with or without masks, who will walk from shop to shop, with some intention and some of them will be virus carriers
- virus, which will spread in places where people go through (depending on the closed/open area, the infection rates will differ)
GOAL of the simulation
- answer the question: "Where is the virus spread more significant? At the market place, or at the shopping mall?"
TOOL used for the simulation
- Agent based simulation
Author: Angel Kostov, xkosa20
Effects of COVID-19 vaccination on the spread of infection
Currently, there is a new wave of the COVID-19 pandemic. Although a certain percentage of the population has already been vaccinated, this is not enough to prevent further COVID-19 measures. The purpose of the simulation is to show how COVID-19 vaccination affects the spread of the pandemic. I will use an agent-based model to simulate the scenario based on existing scientific data. In addition, current COVID-19 measures are considered.
The goal is:
- to determine how vaccination reduces the spread of infection.
Author: Laura Kundmueller
Simulation of genetic algorithm: Travelling Salesman Problem
The topic of this simulation is an old graph problem, Travelling Salesman Problem. My approach would be based on genetic learning algorithm. A random map will be generated at the start. Salesman is travelling in a car with some gas. The gas is used as he travels, it can be recharged at gas stations but it costs money. The map contains some hills and flat roads, which have a different cost of gas when going through.
The goal is:
- to find the optimum path between the towns.
The parameters are:
- number of agents (travelling salesmen)
- gas in car
- number of towns
- number of hills
- number of gas stations
Optimizing the process of baking wedding sweets
Simulation There is a wedding tradition in Czech Republic of baking wedding sweets and then handing them out to the guests of the weeding as a form of invitation. Process of baking usually takes whole day and several helpers in the kitchen are needed. Into paper baskets are usually packaged two types of sweets: several small ones with 3 different flavours and one so-called "rohový koláč". Which are then delivered by the bride to wedding guests. For the purpose of this simulation are process and needed ingredients simplified.
The goal is: The goal is to optimize the number of helpers in the kitchen and find optimal amount of basic ingredients for specified number of guests.
Method: Discrete simulation - SIMPROCESS
- paper baskets
- baking trays
- plum jam
- poppy seed filling
- order for paper basket
- preparing sweets: small ones (3 different flavours), "rohove kolače" sweets (using all flavours)
- baking in the oven
- sugar coating
Carsharing company fleet optimization
Recently, carsharing becomes more and more popular in large cities. Short-term rental (from several minutes to 24 hours) of a car with possibility to drop it anywhere in the allowed area in the city attracts people who for some reasons do not want to use their own vehicles. However, it is not always convenient. If the fleet is relatively small, the probability that a car will be somewhere close by is also quite low. Cars also must be refueled or recharged sometimes by external staff, which would increase cost of the fleet maintenance with increasing of the fleet size.
The proposing agent-based simulation will reproduce real situation with shared cars. Two types of agents are planned:
- cars with different states (waiting, in rent, maintenance) and characteristics (mileage, fuel level)
- drivers - users of the service, who rent the cars and have their own behavior, including decision making on taking a car, driving style, and so on.
Some data for the model will be obtained as personal observations of two carsharing services operating in Prague, Anytime and Uniqway (for example, number of available cars, which is visible in mobile applications). Another source of data would be statistics collected by other services abroad, for example, by operating in Russia service Yandex.Drive.
The goal is: to find out optimal fleet size and structure and price policy to maximize revenue of a carsharing company.
Method: agent-based simulation - NetLogo