Fully distributed Nash equilibrium seeking over time-varying communication networks with linear convergence rate

We design a distributed algorithm for learning Nash equilibria over time-varying communication networks in a partial-decision information scenario, where each agent can access its own cost function and local feasible set, but can only observe the actions of some neighbors. Our algorithm is based on projected pseudo-gradient dynamics, augmented...

Encoding OAS: Solving order acceptance and scheduling with modified black box approaches

In many real world scheduling problems there exist hard deadlines after which tasks can no longer be performed. Conversely, not all tasks are necessarily required to be scheduled. Furthermore, the problem investigated in this thesis includes sequence dependent setup times, an aspect reminiscent of the Travelling Salesperson problem. These...

Simulating smart charging optimization for electric vehicles: A quantification and statistical analysis of the cost reduction and emission reduction potential of an aggregated Dutch EV fleet

The objective of this research is to understand what smart charging can bring business and society at large. In close collaboration with the largest fleet operator in the world and a commercial aggregator, the impact of smart charging on both cost reduction and carbon reduction was simulated for an electric vehicle (EV) fleet in 2018. The...

Side-Channel Attacks using Convolutional Neural Networks: A Study on the performance of Convolutional Neural Networks on side-channel data

Side-Channel Attacks, are a prominent type of attacks, used to break cryptographic implementations on a computing system. They are based on information "leaked" by the hardware of a computing system, rather than the encryption algorithm itself. Recent studies showed that Side-Channel Attacks can be performed using Deep Learning models. In this...

Model predictive control approaches for urban traffic networks: A comparison between optimization algorithms

In this thesis, the effect of traffic light control on urban networks is investigated, with the main focus on the optimization algorithms used to solve the optimization problems resulting from a model predictive control approach. This will be done by using a macroscopic traffic flow model (the S-Model) to simulate the real-time traffic flows...

Graded Structures: The design of a grid shell that uses gradual densification to enhance the performance of the overall structure

This graduation thesis aimed to find an answer to the following question: 'How to design a grid shell for the canopy of a stadium that uses performance based densification to enhance the performance of the structure?'. De Nieuwe Kuip in Rotterdam is used as a case study to put the findings into practice. In order to answer this question a...

Nested algorithms for optimal reservoir operation and their embedding in a decision support platform

Reservoir operation is a multi-objective optimization problem traditionally solved with dynamic programming (DP) and stochastic dynamic programming (SDP) algorithms. The thesis presents novel algorithms for optimal reservoir operation named nested DP (nDP), nested SDP (nSDP), nested reinforcement learning (nRL) and their multi-objective (MO)...

Chemometric Optimization Studies in Catalysis Employing High-Throughput Experimentation

The main topic of this thesis is the investigation of the synergies between High-Throughput Experimentation (HTE) and Chemometric Optimization methodologies in Catalysis research and of the use of such methodologies to maximize the advantages of using HTE methods. Several case studies were analysed including: 1) the exploration of a terpene...