Constructing level-2 phylogenetic networks from trinets

Phylogenetic networks are a generalization of evolutionary trees that can be used to represent reticulate events. Level-k phylogenetic networks are such networks, but with a at most k reticulations per biconnected component of the network. For level-1 networks there exists an algorithm, called TriLoNet (Trinet Level-one Network algorithm), that...

A greedy randomized destroy and repair heuristic for the dial-a-ride problem with transfers

Automated vehicles have the potential to create a future in which most cars are shared instead of being individually owned and used. The advantages of vehicle sharing are expected to be multiple: reduced traffic, freed-up parking space, safer trips and a lower environmental impact of traveling. The dial-a-ride problem with transfers (DARP-T)...

Learning variable selection rules for the branch-and-bound algorithm using reinforcement learning

Mixed Integer Linear Programming (MILP) is a generalization of classical linear programming where we restrict some (or all) variables to take integer values. Numerous real-world problems can be modeled as MILPs, such as production planning, scheduling, network design optimization and many more. MILPs are, in fact, NP-hard. State-of-the-art...

Computational Strategies for Model Predictive Control on Switching Max-plus Linear Systems

A switching max-plus linear model is a framework to describe the discrete dynamics of the timing of events. To influence these systems one can choose the routes of jobs and the orderings of operations as input for the system. In this thesis the techniques of model predictive control are used to find good input values. The problem of finding the...

Formal verification of upper bounds on translative packing densities

Packing problems are concerned with filling the space with copies of a certain object, so that the least amount of space stays unoccupied. The famous Kepler conjecture asserts that the cannonball packing of spheres is the most efficient packing achievable, and was recently formally proven by Hales.<br/><br/>Dostert, Guzman, Oliveira Filho and...

Towards Self-Learning Model-Based Evolutionary Algorithms

Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostly used to repeatedly optimize different instances of a single problem class, a setting in which specialized algorithms generally perform better. In this paper, we introduce the...

Optimizing Outbound Baggage Handling at KLM

In order to handle the increase in baggage units, KLM needs to optimize the outbound baggage handling process. This study focuses on accurately modeling the outbound baggage handling process while minimizing the number of employees working on this process. The results are based upon data on the outbound baggage handling process at KLM in 2018....

Parallel Machine Scheduling: with Partition Constraints

Container-to-mode assignment on a synchromodal transportation network: a multi-objective approach

In this report we present an interactive multi-objective optimization tool that was developed as part of this graduation project. This appliance is meant to be used as a decision support tool for transportation planners working on a synchromodal transportation network on the container-to-mode assignment, where different attributes are considered...