Dynamic Time Slot Management (DTSM) is a system often used in online retail to manage the delivery of goods to customers. With DTSM customers arrive over time and place orders. They get presented with a set of time slots and the customer picks the time slot in which he wants the goods to be delivered to his home. A DTSM system creates a time slot offer for the customers based on the current delivery schedule, which is a solution to a Vehicle Routing Problem (VRP). Each accepted customer is added to this delivery schedule. This delivery schedule gets periodically optimized by an optimization algorithm. During this optimization, new customers may arrive and receive time slots based on the non-optimized schedule, which causes a discontinuity in the system. In this thesis, five different procedures have been created that deal with this problem. The impact of the different procedures is analyzed by simulating the DTSM system and comparing the final delivery schedules. Solving the problem by leaving out the optimization or insertion step of the DTSM system is outperformed by all procedures. Procedures that barely make use of the optimized schedule accept the least number of customers. The procedure that delays customers, which makes it similar to the theoretical setting, accepts the most customers, but this comes at the cost of poor customer service. The most promising results are found by the procedure that inserts new customers into the optimized schedule. Enhancing this procedure with a merge algorithm improves the performance slightly.

In this dissertation we look at the seriation problem and the applications of this problem. Given a set of items, we try to find an ordering based on the similarity between the items. We start by explaining the mathematical theory behind the seriation problem. Then we describe a couple of different methods that can be used to find a solution for the problem. After that, we apply these methods to various different datasets. The results of these tests will be analysed.
Solving a seriation problem can be an alternative way to already existing methods when finding a ranking of items for a given dataset. The goal is to find out if is also a viable method to use in practice.