In the Netherlands, online groceries are becoming increasingly popular, as are the challenges grocery companies face in meeting customers' rising demand for smaller and cheaper time slots while maintaining thin profit margins due to a highly competitive market. Customer choice modelling will be used to identify customers' behaviour and control the trade-offs between customer attractiveness and profitability with demand management. As there are parametric and non-parametric models, including Machine Learning, to identify the behaviour, they will be compared to define which model represents customer behaviour best. Based on the F1-score, it is evaluated that the Multinomial logit (MNL) and Neural Network (NN) models outperform the created Benchmark model, derived from assumptions from the data, to ensure added value in predicting performance. Since it is found that the advanced predictive models can provide a better understanding of customer behaviour and identify critical customer characteristics, it will be tested whether these models enhance the simulation closer to reality or produce similar results to the Benchmark model, and if so, whether they can be used to optimise the offer strategy. In essence, does the learned behaviour impact the routes and number of offered slots, and can it be utilised to optimise the provided set of offers? A simulation tool is conducted to determine this, which uses real-selected customer time slots to assess any differences from reality and collect key performance indicators (KPIs) for evaluation. The results of the simulations show that incorporating advanced choice models in the simulations adds value and brings the outcomes closer to reality. As a result, a first attempt is made with two test scenarios to determine whether the advanced choice models can be used to optimise the offer strategy. Promising outcomes are found by analysing the results. However, further research is needed to assess the exact impact of the strategies. In future research, it is recommended to determine the influence of using more historical data and data from other companies in combination with other choice models. Also, it is recommended to consider different strategies for optimising the offer set based on incentives or other features.

Inverse Learning is implemented in order to learn a control/decision policy (in the integer space) from an Expert Agent. The Learner Agent assumes that the Expert is acting minimizing an unknown cost function and tries to approximate it, through its own parametrized version of it. Learning can be performed in two different ways: offline (exploiting a training set containing Expert data) and online, in which the Learner Agent is directly controlling the system while learning the policy, exploiting corrective advice from the Expert. We propose three different learning algorithms and draw a comparison between them, as well as assess their performance against the Expert. We use our Agent for two different applications: control of a dynamical system (1) and classic ML classification/regression tasks (2). For application (1), our case study is the Heavy Shell Oil Fractionator system, with an MPC Expert Agent. For application (2), we train and test our Agent on several real data-sets available online, with tasks such as medical diagnosis, social media comment volume prediction, fault detection/isolation and multi-class classification.