6 papers · 1 filter
Recurrent State Encoders for Efficient Neural Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Lars Schmidt-Thieme
The primary paradigm in Neural Combinatorial Optimization (NCO) consists of construction methods, where a neural network is trained to sequentially add one solution component at a…
On Distributional Dependent Performance of Classical and Neural Routing Solvers
Daniela Thyssens, Tim Dernedde, Wilson Sentanoe +1
Neural Combinatorial Optimization aims to learn to solve a class of combinatorial problems through data-driven methods and notably through employing neural networks by learning the…
Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
Christian Klötergens, Tim Dernedde, Lars Schmidt-Thieme +1
Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While re…
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Sören Dittrich +2
Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have…
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Daniela Thyssens, Tim Dernedde, Jonas K. Falkner +1
Neural Combinatorial Optimization has been researched actively in the last eight years. Even though many of the proposed Machine Learning based approaches are compared on the same…
RP-DQN: An application of Q-Learning to Vehicle Routing Problems
Ahmad Bdeir, Simon Boeder, Tim Dernedde +3
In this paper we present a new approach to tackle complex routing problems with an improved state representation that utilizes the model complexity better than previous methods. We…