West IT Solutions is expanding into a new focus area, ”Smart Planning”. They want to develop new modules for the Enterprise Resource Planning software they resell, Odoo. Odoo allows companies to manage their projects, employees and other resources. The goal of this project was to create a software product that assists users in creating plannings for their projects in Odoo. The product should offer the following features: Visualise a planning for the user’s projects, Facilitate manually adapting the planning and Propose an automatically generated improvement of the planning. During the research phase of the project we met with a client of West, TNO. TNO explained they are performing research for the Dutch navy to create software that assists planners in creating schedules for battleships. With this case problem in mind, we created a list of requirements our software product should adhere to. We discovered that the case problem West provided us with is a subset of the case problem TNO provided. This meant that if we created a model for the case problem of TNO, the model would work for the case problem of West as well. After the research phase was completed, we started development on the software product. The development phase consisted of seven weeks, and in each week new functionalities were added to the product. In addition to adding new functionalities, we also kept refining our models to improve the performance of the automatic planning generation. The final software product consists of an interface that visualises a planning, a model that can be entered into external algorithms to automatically generate plannings and a module that allows users to import existing projects from Odoo. The user can view their planning through a visualisation that allows them to modify it using a drag and drop interaction. These modifications are setting preferences, like starting times for activities or the assignment of certain persons to certain activities. Once the user has set their preferences, they can ask the software to find an optimised planning that keeps their preferences in mind. At the end of the project, we gave a demo to TNO and they were impressed with the results of the project. West IT was also happy with the delivered product and is interested in further developing it

Machine learning is used for security purposes, to differ between the benign and the malicious. Where decision trees can lead to understandable and explainable classifications, an adversary could manipulate the model input to evade detection, e.g. the malicious been classified as the benign. State-of-the-art techniques improve the robustness by taking these adversarial attacks into account when building the model. In this work, I identify three factors contributing to the robustness of a decision tree: feature frequency, shortest distance between malicious leaves and benign prediction space, and impurity of benign prediction space. I propose two splitting criteria to improve these factors and suggest a combination with two trade-off approaches to balance the use of these splitting criteria with a common splitting criterion, Gini Impurity, in order to balance accuracy and robustness. These combinations allow building robuster models against adversaries manipulating the malicious data without considering adversarial attacks. The approaches are evaluated in a white-box setting against a decision tree and random forest, considering an unbounded adversary where robustness is measured using a L1-distance norm and the false negative rate. All combinations lead to robuster models at different costs in terms of accuracy, showing that adversarial attacks do not need to be taken into account to improve robustness. Compared to state-of-the-art work, the best approach achieves on average 3.17% better accuracy with an on average lower robustness of 5.5% on the used datasets for a single decision tree. In a random forest the best approach achieves on average 2.87% better robustness with a 2.37% better accuracy on the used datasets compared to the state-of-the-art work. The state-of-the-art work does not seem to affect all of the identified factors, which leaves room for even robuster models than currently existing.