Publications (17)
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
André Hottung, Kevin Tierney
Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuris…
VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
André Hottung, Federico Berto, Chuanbo Hua +9
Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based…
A Survey of Methods for Automated Algorithm Configuration
Elias Schede, Jasmin Brandt, Alexander Tornede +4
Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of A…
Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem
André Hottung, Shunji Tanaka, Kevin Tierney
The container pre-marshalling problem (CPMP) is concerned with the re-ordering of containers in container terminals during off-peak times so that containers can be quickly retrieve…
Learning How to Optimize Black-Box Functions With Extreme Limits on the Number of Function Evaluations
Carlos Ansotegui, Meinolf Sellmann, Tapan Shah +1
We consider black-box optimization in which only an extremely limited number of function evaluations, on the order of around 100, are affordable and the function evaluations must b…
ASlib: A Benchmark Library for Algorithm Selection
Bernd Bischl, Pascal Kerschke, Lars Kotthoff +8
The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set…
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…
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park +30
Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement lear…
Solving the unit-load pre-marshalling problem in block stacking storage systems with multiple access directions
Jakob Pfrommer, Anne Meyer, Kevin Tierney
Block stacking storage systems are highly adaptable warehouse systems with low investment costs. With multiple, deep lanes they can achieve high storage densities, but accessing so…
On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI
André Artelt, Martin Olsen, Kevin Tierney
Providing clear explanations to the choices of machine learning models is essential for these models to be deployed in crucial applications. Counterfactual and semi-factual explana…
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization
André Hottung, Mridul Mahajan, Kevin Tierney
Reinforcement learning-based methods for constructing solutions to combinatorial optimization problems are rapidly approaching the performance of human-designed algorithms. To furt…
Rethinking Positional Encoding for Neural Vehicle Routing
Chuanbo Hua, Federico Berto, Andre Hottung +8
Transformer-based models have become the dominant paradigm for neural combinatorial optimization (NCO) of vehicle routing problems (VRPs), yet the role of positional encoding (PE)…
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda +6
This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model f…
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Jasmin Brandt, Elias Schede, Viktor Bengs +3
We study the algorithm configuration (AC) problem, in which one seeks to find an optimal parameter configuration of a given target algorithm in an automated way. Recently, there ha…
Efficient Active Search for Combinatorial Optimization Problems
André Hottung, Yeong-Dae Kwon, Kevin Tierney
Recently numerous machine learning based methods for combinatorial optimization problems have been proposed that learn to construct solutions in a sequential decision process via r…
Neural Deconstruction Search for Vehicle Routing Problems
André Hottung, Paula Wong-Chung, Kevin Tierney
Autoregressive construction approaches generate solutions to vehicle routing problems in a step-by-step fashion, leading to high-quality solutions that are nearing the performance…
Simulation-guided Beam Search for Neural Combinatorial Optimization
Jinho Choo, Yeong-Dae Kwon, Jihoon Kim +4
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches ca…