20 citations · 60 across the 18 of their papers we have counts for
19 papers
Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction
Ya Song, Laurens Bliek, Yaoxin Wu +1
Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spa…
Optimal Transport-based Permutation-Invariant Bayesian Optimization of Offshore Wind Farm Layouts
Antonio Candelieri, Laurens Bliek
Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-convex objective function. Howev…
End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW
Abdo Abouelrous, Laurens Bliek, Yaoxin Wu +1
In this work, we consider learning-based applications in routing to solve a Vehicle Routing variant characterized by stochasticity and multiple objectives. Such problems are repres…
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application
Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor +2
Column Generation (CG) is a popular method dedicated to enhancing computational efficiency in large scale Combinatorial Optimization (CO) problems. It reduces the number of decisio…
Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing
Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor +2
In this paper, we address the problem of Column Generation (CG) using Reinforcement Learning (RL). Specifically, we use a RL model based on the attention-mechanism architecture to…
Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks
Ya Song, Laurens Bliek, Yingqian Zhang
Algorithm selection is a well-known problem where researchers investigate how to construct useful features representing the problem instances and then apply feature-based machine l…
Digital Twin Applications in Urban Logistics: An Overview
Abdo Abouelrous, Laurens Bliek, Yingqian Zhang
Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and co…
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
Remco Coppens, Robbert Reijnen, Yingqian Zhang +2
Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good s…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Laurens Bliek, Paulo da Costa, Reza Refaei Afshar +19
This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCA…
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Laurens Bliek, Arthur Guijt, Rickard Karlsson +2
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulatio…
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems
Rickard Karlsson, Laurens Bliek, Sicco Verwer +1
One method to solve expensive black-box optimization problems is to use a surrogate model that approximates the objective based on previous observed evaluations. The surrogate, whi…
Black-box Mixed-Variable Optimisation using a Surrogate Model that Satisfies Integer Constraints
Laurens Bliek, Arthur Guijt, Sicco Verwer +1
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evalua…
Black-box Combinatorial Optimization using Models with Integer-valued Minima
Laurens Bliek, Sicco Verwer, Mathijs de Weerdt
When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Sp…
Automatic Delay Tuning of a Novel Ring Resonator-based Photonic Beamformer for a Transmit Phased Array Antenna
L. Bliek, S. Wahls, I. Visscher +5
We present a novel photonic beamformer for a fully integrated transmit phased array antenna, together with an automatic procedure for tuning the delays in this system. Such an auto…
Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions
Laurens Bliek, Hans R. G. W. Verstraete, Michel Verhaegen +1
This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogat…
Calculations for OPF with Adjustable Tap Ratios
Laurens Bliek
In this document, the calculations of the AC power balance and flow equations used by MATPOWER will be extended to include adjustable tap ratios.
Exploration and Exploitation in Visuomotor Prediction of Autonomous Agents
Laurens Bliek
This paper discusses various techniques to let an agent learn how to predict the effects of its own actions on its sensor data autonomously, and their usefulness to apply them to v…
Universal Approximation Using Shuffled Linear Models
Laurens Bliek
This paper proposes a specific type of Local Linear Model, the Shuffled Linear Model (SLM), that can be used as a universal approximator. Local operating points are chosen randomly…