Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows

no code implementations • 21 Jul 2023 • Mohammed Ghannam, Ambros Gleixner

In this paper we adapt the Hybrid Genetic Search (HGS) algorithm, a successful heuristic for VRPTW, to the dynamic variant.

Online Learning for Scheduling MIP Heuristics

no code implementations • 4 Apr 2023 • Antonia Chmiela, Ambros Gleixner, Pawel Lichocki, Sebastian Pokutta

In this work, we propose an online learning approach that adapts the application of heuristics towards the single instance at hand.

Scheduling

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

2 code implementations • 4 Mar 2022 • Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros Gleixner, Aleksandr M. Kazachkov, Elias Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun

Combinatorial optimization is a well-established area in operations research and computer science.

BIG-bench Machine Learning Combinatorial Optimization

Learning to Schedule Heuristics in Branch and Bound

1 code implementation • NeurIPS 2021 • Antonia Chmiela, Elias Boutros Khalil, Ambros Gleixner, Andrea Lodi, Sebastian Pokutta

Compared to the default settings of a state-of-the-art academic MIP solver, we are able to reduce the average primal integral by up to 49% on two classes of challenging instances.

Decision Making Scheduling

Learning to Schedule Heuristics in Branch-and-Bound

1 code implementation • NeurIPS 2021 • Antonia Chmiela, Elias B. Khalil, Ambros Gleixner, Andrea Lodi, Sebastian Pokutta

In this work, we propose the first data-driven framework for scheduling heuristics in an exact MIP solver.

Decision Making Scheduling