Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization
Authors: Zijun Liao, Jinbiao Chen, Debing Wang, Zizhen Zhang, Jiahai Wang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Job-shop Scheduling Problem (JSP), Traveling Salesman Problem (TSP), and Flexible Job-shop Scheduling Problem (FJSP) show BOPO outperforms state-of-the-art neural methods, reducing optimality gaps impressively with efficient inference. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-sen University, China. Correspondence to: Zizhen Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 BOPO Training |
| Open Source Code | Yes | Our implementation of BOPO using Py Torch and trained models for each problem are available.1 1https://github.com/L-Z-7/BOPO |
| Open Datasets | Yes | For evaluation, we use three standard JSP benchmarks: Taillard s (TA) (Taillard, 1993), Lawrence s (LA) (Lawrance, 1984), and Demirkol s (DMU) (Demirkol et al., 1998). |
| Dataset Splits | Yes | We generate a training dataset of 30000 instances following SLIM (Corsini et al., 2024), consisting of 6 shapes (n m) in {10 10, 15 10, 15 15, 20 10, 20 15, 20 20} with 5000 instances per shape. During training, we generate additional 100 different instances per shape from the same shape set for validation. |
| Hardware Specification | Yes | Experiments were conducted on a Linux system with an NVIDIA TITAN Xp GPU and an Intel(R) Xeon(R) E52680 CPU. |
| Software Dependencies | No | Our implementation of BOPO using Py Torch and trained models for each problem are available. (Only mentions PyTorch, but no version number, and no other specific software/library versions). |
| Experiment Setup | Yes | We employ the Adam optimizer (Kingma & Ba, 2014) with learning rate η = 0.0002 and train the neural model for 20 epochs. We set the solution number of hybrid rollout B = 256, the number of filtered solutions K = 16, batch size of D = 1. |