Roparun is a Foundation that organizes a yearly relay race from Paris, Hamburg and Almelo to Rotterdam in order to raise money for a variety of causes related to palliative care. They requested for an application that could create the start schedule of the race. In this thesis, the problem is analyzed and a greedy based solution is presented. Furthermore, the process to create the application, and the application's architecture is described.

The value of data has increased enormously over the last couple of years. Many datasets contain valuable information that can, for example, be used to make forecasts. In this thesis, the dataset of a company in the self-storage industry is analyzed. The company offers customers rental storage facilities, such as lockers, rooms, containers at several locations in the Netherlands. The rooms, also called units, are characterized by three features: location, volume, and floor. The dataset contains for each storage facility the entire renting history of each self-storage unit. For the company, it is of interest to understand and predict the demand of its customers, such that it can flexibly adjust the prices of the rooms and services based on demand. Beyond, forecasting users' demand for various types of products is a common and essential problem in many different domains (e.g., recommendation systems, transportation systems). This thesis aims to predict the demand for the next week by applying white-box and black-box models. The problem is represented as a temporal weighted bipartite network prediction problem. Specifically, the goal is to predict the network structure at a time T+1 based on the bipartite network observed at time T-k+1, T-k+2, ..., T, where k is an integer and needs to be optimized such that the prediction error is minimized. By analyzing the data in its temporal dimension, using the autocorrelation and cross-correlation among different storage locations, floors and volumes, it was shown that the autocorrelation is high and the cross-correlation is low. This suggests that the temporal bipartite network is possibly predictable. We have explored different state-of-the-art predictive techniques. Markov chain model, LSTM, and ConvLSTM have been selected because of their fundamental difference in the way they learn and predict. A performance comparison is given where the techniques have been applied on the storage data, and it shows that LSTM outperforms the Markov chain and ConvLSTM based on the following evaluation metrics: RMSE, MAE, and accuracy. According to our dataset, higher predictability was achieved when only the data of a single link was exploited. The Markov chain and the LSTM utilize the information of a single link to predict. On the contrary, the ConvLSTM utilizes the information of the entire network to predict. The low cross-correlation between the links explains why the LSTM outperforms the ConvLSTM. The ConvLSTM tries to capture spatio-temporal dependencies, while this, in general, does not contain much valuable predictive information. Thus, the model is introduced to more noise, making it harder to predict accurately. The LSTM also outperforms the Markov chain model, which is used as a baseline method. This proves that it is beneficial to use a complex deep learning model for this dataset to predict. However, the Markov chain performs comparable to the ConvLSTM, showing that a black-box model does not always outperform a white-box model. This emphasizes that the most suitable predictive algorithm depends on the statistical properties of the dataset. The theoretical upper bound of the predictability of the network is computed. It is the upper bound that can be used to compare the realized performance to the maximum achievable prediction performance for any predictive algorithm. The difference between the performance of the best performing algorithm to our dataset, LSTM, and the theoretical upper bound is still large, indicating that there is still room for improvement.