16 citations · 40 across the 10 of their papers we have counts for
11 papers
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…
Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing Problems
Jonas K. Falkner, Lars Schmidt-Thieme
In recent years new deep learning approaches to solve combinatorial optimization problems, in particular NP-hard Vehicle Routing Problems (VRP), have been proposed. The most impact…
Neural Capacitated Clustering
Jonas K. Falkner, Lars Schmidt-Thieme
Recent work on deep clustering has found new promising methods also for constrained clustering problems. Their typically pairwise constraints often can be used to guide the partiti…
Attention, Filling in The Gaps for Generalization in Routing Problems
Ahmad Bdeir, Jonas K. Falkner, Lars Schmidt-Thieme
Machine Learning (ML) methods have become a useful tool for tackling vehicle routing problems, either in combination with popular heuristics or as standalone models. However, curre…
Learning to Control Local Search for Combinatorial Optimization
Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir +1
Combinatorial optimization problems are encountered in many practical contexts such as logistics and production, but exact solutions are particularly difficult to find and usually…
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning
Christian Löwens, Inaam Ashraf, Alexander Gembus +3
This work presents solutions to the Traveling Salesperson Problem with precedence constraints (TSPPC) using Deep Reinforcement Learning (DRL) by adapting recent approaches that wor…
Large Neighborhood Search based on Neural Construction Heuristics
Jonas K. Falkner, Daniela Thyssens, Lars Schmidt-Thieme
We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem wi…
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size
Daniela Thyssens, Jonas Falkner, Lars Schmidt-Thieme
Learning to solve combinatorial optimization problems, such as the vehicle routing problem, offers great computational advantages over classical operations research solvers and heu…
Improving Hyperparameter Optimization by Planning Ahead
Hadi S. Jomaa, Jonas Falkner, Lars Schmidt-Thieme
Hyperparameter optimization (HPO) is generally treated as a bi-level optimization problem that involves fitting a (probabilistic) surrogate model to a set of observed hyperparamete…
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…
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention
Jonas K. Falkner, Lars Schmidt-Thieme
Many real-world vehicle routing problems involve rich sets of constraints with respect to the capacities of the vehicles, time windows for customers etc. While in recent years firs…