Assignments WS 2019/2020

Simulation Proposal - Shopping Centre Management

Each store in a shopping centre pays rent to the shopping centre landlord. Final rent that a store has to pay is calculated as a sum of fixed rent and percentage rent (based on a percentage of store sales). A company that manages shopping centres is profitable when all tenants of the centre are profitable. Sales among tenants are distributed by normal distribution with one outlier, which has double the sales of the second best. This tenant is typical for each shopping centre and is called an anchor tenant. Usually it is a hypermarket. The managing company has services costs, maintenance costs, marketing costs, which are distributed among the tenants in addition to rent. If a tenant can't afford to pay rent + costs, he will terminate lease and managing company loses profit from rent. Other variables affecting the profit are for example footfall of the centre (number of visitors), number of stores, size of the store, conversion ratio (ratio of people walking in to people buying something), average basket (average money spent per customer), anchor tenant sales ratio to all sales, average length of lease, percentage rent, risk of a tenant going out of business. Since getting sales data of each tenant in a shopping centre is not possible (confidential information), I will build the model with a reasonable deal of abstraction on freely available financial data and retail reports about shopping centers. I will study what percentage rent and fixed rent per square meter should the managing company employ to increase the probability of staying profitable in long term.

Title: Shopping Centre Management

Author: Dominik Tkáč, TKAD01 (talk) 00:00, 13 December 2019 (CET)

Model type: Stock and flow diagram

Modeling tool: Vensim

Oleg.Svatos (talk) 22:03, 13 December 2019 (CET) Approved. Make sure that you describe in detail in the report, how you have derived the equations from the available data.

Simulation Proposal - Indian elephant population in Thailand

During my trip to Thailand I was very touched by a lecture on decrease in Indian elephant population in Thailand and their utilization in present mostly in tourism. Indian elephant population has dramatically decreased over the course of 20th century mostly due to a change of a position of elephant in human's life as well as significant reduction of their natural habitat. Based on available data of elephant's population, natural and human threats and major influences on the elephant's numbers I want to find main factors affecting population of elephants in Thailand as well as predict future population changes with increase in sustainable tourism.

Title: Orders completion in a warehouse

Author: Ján Káva Xkavj12 (talk) 12:30, 11 December 2019 (CET) Edit: Xkavj12 (talk) 17:55, 22 December 2019 (CET)

Model type: Stock and Flow Diagram

Modeling tool: Vensim

Simulation Proposal – Cotton Processing Quality: Cotton, Seed, Lint, Trash

This simulation is inspired by previous experience as process management specialist in Cotton Processing Company in Azerbaijan. According to process, cotton was received from farmers and price was indicated based on the moisture and trash level of the raw material. Then raw cotton is going through individual steps of processing. Different types of products are being manufactured from the raw cotton with different qualities and prices. The purpose of simulation is to analyze possible income of the company and create decision making threshold regarding acceptance and price of raw cotton received from farmers.

Title: Cotton Processing Quality: Cotton, Seed, Lint, Trash

Author: Ibrahim Aghazada

Model type: Stock and flow diagram

Modeling tool: Vensim

Oleg.Svatos (talk) 15:50, 15 December 2019 (CET) The idea is OK, just do not forget to make the simulation reasonably complex so that it would be useful in practice. Make sure that you describe in detail in the report, how you have derived the equations from the available data. Approved.

Simulation Proposal - Atlantic Bluefin Tuna Population

The population of tuna worldwide is decreasing due to being overfished. Yet some studies claim, that overfishing is not the main cause of the tuna population reduction. Christelle Ravier and Jean-Marc Fromentin in their paper "Long-term fluctuations in the eastern Atlantic and Mediterranean bluefin tuna population" came to the conclusion that the bluefin tuna population may be influence by biotic and environmental factors more than by overexploitation. This simulation will use available data of population size, juvenile death, environmental influences, fishing statistics and other to investigate whether overfishing is the main culprit for bluefin tuna population decline.

Title: Atlantic Bluefin Tuna Population

Author: Petra Vokálová, Vokp00 (talk) 07:43, 9 December 2019 (CET)

Model type: Stock and flow diagram

Modeling tool: Vensim

Oleg.Svatos (talk) 22:09, 11 December 2019 (CET) Approved. Make sure that you describe in detail in the report, how you have derived the equations from the available data (proving so that they are based on relevant data).

Simulation Proposal - Population growth/decrease in EU in upcoming years

This simulation aims to predict future development of population in EU. Concerns about the future of EU population have risen in recent years, since population gets older in first world countries and it does not make economical sense to have many children. Based on available statistical data about population growth, fertility decrease, immigration etc., this simulation should display trends of population growth or decrease, also in case of changes in certain variables, such as government actions (child support, better maternity leaves). Main source of data for this simulation will be Eurostat, an EU statists portal, I will also use "Demographic outlook for the European Union 2019", a European Parliament document.

Title: Population growth/decrease in EU in upcoming years

Author: Václav Pleskač, plev00, 19 December 2019

Model type: Dynamic system

Modeling tool: Vensim

Oleg.Svatos (talk) 11:56, 20 December 2019 (CET) The idea is OK, just do not forget to make the simulation reasonably complex so that it would be useful in practice. Make sure that you describe in detail in the report, how you have derived the equations from the available data. Approved.

Simulation Proposal - Crop Rotation in sustainable farming

Crop rotation is based on growing a series of different types of crops in the same area in sequential seasons. The planned rotation may vary from a growing season to a few years or even longer periods. It is one of the most effective agricultural control strategies that is used in preventing the loss of soil fertility. It also helps in reducing soil erosion and increases crop yield. Planning an effective crop rotation requires weighing fixed and fluctuating production circumstances: market, farm size, labor supply, climate, soil type, growing practices, etc. In this simulation I will try to find parameters which have impact on the whole process of crop rotation with goal to find model providing desired outputs - high soil fertility, low soil erosion, good quality crops, high crop field and therefore higher revenue.

Title: Crop Rotation in sustainable farming

Author: Michal Šimánek, Simm04 (talk) 20:33, 10 December 2019 (CET)

Model type: Stock and flow diagram

Modeling tool: Vensim

Oleg.Svatos (talk) 22:13, 11 December 2019 (CET) What data will you build (derive the equations) your simulation on?

Simm04 (talk) 20:02, 12 December 2019 (CET) I would use available dataset from https://www.nature.com/articles/s41598-017-14271-6#Sec16

Oleg.Svatos (talk) 22:00, 12 December 2019 (CET) OK. Approved. Make sure that you describe in detail in the report, how you have derived the equations from the available data (proving so that they are based on relevant data).

Simulation Proposal - Time Series Model Building Process

Linear dynamic models, including univariate case Autoregressive Integrated Moving Average (ARIMA) models and multivariate case Vector AutoRegression (VAR) models, are mainstream approaches to modeling and forecasting time series in econometric practice. They are popular mainly for their simplicity. However, the model building process is not completely straightforward, and the practitioner has to make some decisions such as the transformation of time series or selecting a number of lags (orders p and q in ARIMA model). Recommendations for the decisions are usually formulated as 'cookbooks.' Since the different sources recommend different decision rules, the cookbooks may provide contradictory results. I propose to study decision rules for orders of autoregressive univariate models using MC simulation. Specifically, I will create a set of data-generating processes, simulate the time series, build the models using the cookbooks, and compare the success rate of building a correct model. Based on the success rates of decision rules, I will compare the relative performance of different rules. Additionally, I will use different data-generating processes to study performance under varied assumptions. The choice rules will include General-To-Specific (GETS), specific-to-general, Schwarz-Bayesian Information Criterion (SBIC), and Akaike Information Criterion (AIC).

Title: Simulation Proposal – Time Series Model Building Process

Author: Nikola Hažmuková, Hazn00 (talk) 18:59, 13 December 2019 (CET)

Model type: Monte Carlo

Modeling tool: Python

This is on the edge of our scope, however sounds interesting, hence worth to try. Please, just bear in mind that the solution must be an original work for this course. If you would use any part done for any other occasion (another course, work, etc.), it is necessary to distinguish it clearly in your paper. Approved. Tomáš (talk) 15:59, 14 December 2019 (CET)