Cover Image

All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hijazi, Amira, Ozaltin, Osman, Uzsoy, Reha
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Reinforcement-Learning-Based Multiple-Column Selection Strategy for Column Generation
by: Yuan, Haofeng, et al.
Published: (2023)

Machine Learning-Enhanced Ant Colony Optimization for Column Generation
by: Xu, Hongjie, et al.
Published: (2024)

Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling
by: Lu, Jiawei, et al.
Published: (2024)

Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
by: Yang, Yiran, et al.
Published: (2025)

Attention is All You Need to Optimize Wind Farm Operations and Maintenance
by: Kazemian, Iman, et al.
Published: (2024)

FFCG: Effective and Fast Family Column Generation for Solving Large-Scale Linear Program
by: Hu, Yi-Xiang, et al.
Published: (2024)

SPOT: Spatio-Temporal Pattern Mining and Optimization for Load Consolidation in Freight Transportation Networks
by: Cheng, Sikai, et al.
Published: (2025)

Identifying Multi-Hit Cancer Drivers Without Massive Parallelization: A CP, MIP, and Column Generation Framework
by: Willemsen, Rick S. H., et al.
Published: (2026)

Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need
by: Zheng, Xinran, et al.
Published: (2026)

Column Generation for Periodic Timetabling
by: Riedmüller, Stephanie, et al.
Published: (2025)

On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
by: Xu, Ruihan, et al.
Published: (2026)

Belief Samples Are All You Need For Social Learning
by: JafariNodeh, Mahyar, et al.
Published: (2024)

Learning to Control Stabilization in Column Generation
by: Wang, Olivia, et al.
Published: (2026)

Adaptive Stabilization Based on Machine Learning for Column Generation
by: Shen, Yunzhuang, et al.
Published: (2024)

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)

A Reinforcement-learning-based Column Generation Algorithm for Integrated Operating Room Planning and Scheduling
by: Dolatkhah, Mahdi, et al.
Published: (2026)

Adaptive Partitioning in Column Generation for Nested Paths
by: van Rossum, Bart, et al.
Published: (2026)

Improving Generalization and Convergence by Enhancing Implicit Regularization
by: Wang, Mingze, et al.
Published: (2024)

Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
by: Riedl, Konstantin, et al.
Published: (2023)

Minimizing the Weighted Number of Tardy Jobs: Data-Driven Heuristic for Single-Machine Scheduling
by: Antonov, Nikolai, et al.
Published: (2025)

Coordinating Resource Allocation during Product Transitions Using a Multifollower Bilevel Programming Model
by: Khorramfar, Rahman, et al.
Published: (2024)

Serial and Parallel Two-Column Probing for Mixed-Integer Programming
by: Dai, Yongzheng, et al.
Published: (2024)

Integrating Column Generation and Large Neighborhood Search for Bus Driver Scheduling with Complex Break Constraints
by: Kletzander, Lucas, et al.
Published: (2025)

Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards
by: Kim, Jung-hun, et al.
Published: (2021)

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
by: Medyakov, Daniil, et al.
Published: (2025)

Column Generation for the Micro-Transit Zoning Problem
by: Hu, Hins, et al.
Published: (2026)

General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization
by: Ahn, Kwangjun, et al.
Published: (2024)

Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws
by: Wang, Jinbo, et al.
Published: (2026)

Quantizer Design for Finite Model Approximations, Model Learning, and Quantized Q-Learning for MDPs with Unbounded Spaces
by: Bicer, Osman, et al.
Published: (2025)

Design and Scheduling of an AI-based Queueing System
by: Lee, Jiung, et al.
Published: (2024)

Learning Rate Schedules in the Presence of Distribution Shift
by: Fahrbach, Matthew, et al.
Published: (2023)

Drone-Aided Blood Collection Routing Problem: A Column Generation Approach
by: Abbaszadeh, Amirhossein, et al.
Published: (2026)

On Column sufficiency and Extended Horizontal Linear Complementarity Problem
by: Yadav, Punit Kumar, et al.
Published: (2025)

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
by: Kimiaei, Morteza, et al.
Published: (2025)

Improving Convergence and Generalization Using Parameter Symmetries
by: Zhao, Bo, et al.
Published: (2023)

Controllable Machine Unlearning via Gradient Pivoting
by: Hwang, Youngsik, et al.
Published: (2025)

Logarithmic-time Schedules for Scaling Language Models with Momentum
by: Ferbach, Damien, et al.
Published: (2026)

Subsampled Ensemble Can Improve Generalization Tail Exponentially
by: Qian, Huajie, et al.
Published: (2024)

Fragility-aware Classification for Understanding Risk and Improving Generalization
by: Yang, Chen, et al.
Published: (2025)

Sharpness-Aware Minimization: General Analysis and Improved Rates
by: Oikonomou, Dimitris, et al.
Published: (2025)