Reconstructing a minimum reticulation network from phylogenetic trees is used in evolutionary studies. In this thesis, we focus on finding temporal networks using cherry-picking sequences for binary trees with all taxa. Finding such a minimum reticulation temporal network is NP-hard.

We introduce an algorithm to find a minimum reticulation network with a running time of O(2^n poly(n,t)). In addition, this study explores potential enhancements to the algorithm through the utilisation of branch and bound.

Additionally, we introduce a similar algorithm to determine the existence of a temporal phylogenetic network. This algorithm is improved upon by integrating a new concept called cherry growing. This leads to a notable speed-up in performance.

Furthermore, we examine the application of Graph Neural Networks (GNN) in heuristics to find a cherry-picking sequence which can be used to construct a network. This is done by classifying leaves into good, which leads to optimal solutions, and bad leaves. To assess this, two types of data were employed: one simulating evolutionary models and the other employing a fully random approach. The best-performing GNN model has a 97.4% accuracy for evolution-based data and a 79.1% accuracy for random-based data.

The GNN models are implemented as predictors in two classes of heuristics. The first generates a cherry-picking sequence by repeatedly picking leaves. The second class of heuristics is based on a tree search heuristic. This tree-search-based heuristic outperforms the cherry-picking-based heuristic. Furthermore, the GNN heuristics outperform their random variant, even for problems substantially larger than the GNN was trained on.

We also examine the use of GNN in predicting the existence of a phylogenetic temporal network given a set of trees. The best-performing GNN found for this problem has an accuracy of 80.3%.

Supply planning is an NP-Hard problem that is often tackled when dealing with supply chain management. It is a problem with many variations but the core idea is to create a plan that resolves as much demand as possible.
There are different approaches in use to solve a planning problem, one of which is by using heuristics. Heuristics are used to get to a relatively good solution within some feasible amount of time compared to trying to get to an optimal solution. Expertise of the problem is often required to come up with good heuristics or to improve existing ones. When such an expert is not readily available alternatives need to be considered, one of which is machine learning.
The goal of this thesis is to create a machine learning model that can improve the heuristics used in an existing planning algorithm at Outperform. This heuristic decides upon a sequence in which products are included in the planning algorithm. Using a different sequence can lead to vastly different results for the algorithm both in quality and time required. Our focus will be on reducing the amount of time required for the algorithm by learning a heuristic that can provide better sequences. 
To achieve this goal, two models are created and evaluated. These models use imitation learning to replicate the sequences that were the fastest. Due to the lack of an expert to provide the fastest sequences, an oracle is created that attempts to search for a sequence as fast as possible within some feasible amount of time. The trained models are evaluated on several supply chains provided by Outperform.