PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization

1 code implementation • 5 Sep 2024 • Federico Berto, Chuanbo Hua, Laurin Luttmann, Jiwoo Son, Junyoung Park, Kyuree Ahn, Changhyun Kwon, Lin Xie, Jinkyoo Park

Multi-agent combinatorial optimization problems such as routing and scheduling have great practical relevance but present challenges due to their NP-hard combinatorial nature, hard constraints on the number of possible agents, and hard-to-optimize objective functions.

Combinatorial Optimization Decision Making +1

RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

3 code implementations • 29 Jun 2023 • Federico Berto, Chuanbo Hua, Junyoung Park, Laurin Luttmann, Yining Ma, Fanchen Bu, Jiarui Wang, Haoran Ye, Minsu Kim, Sanghyeok Choi, Nayeli Gast Zepeda, André Hottung, Jianan Zhou, Jieyi Bi, Yu Hu, Fei Liu, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Davide Angioni, Wouter Kool, Zhiguang Cao, Qingfu Zhang, Joungho Kim, Jie Zhang, Kijung Shin, Cathy Wu, Sungsoo Ahn, Guojie Song, Changhyun Kwon, Kevin Tierney, Lin Xie, Jinkyoo Park

To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.

Combinatorial Optimization Computational Efficiency +5

Formulating and solving integrated order batching and routing in multi-depot AGV-assisted mixed-shelves warehouses

1 code implementation • 27 Jan 2021 • Lin Xie, Hanyi Li, Laurin Luttmann

Due to the complexity of the integrated problem, we develop a novel variable neighborhood search algorithm to solve the integrated problem more efficiently.