Minimizing the Weighted Number of Tardy Jobs: Data-Driven Heuristic for Single-Machine Scheduling
Fuente:
arXiv
Saved in:
| Main Authors: | Antonov, Nikolai, Šůcha, Prěmysl, Janota, Mikoláš, Hůla, Jan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
by: Bouška, Michal, et al.
Published: (2024)
by: Bouška, Michal, et al.
Published: (2024)
Urgent Samples in Clinical Laboratories: Stochastic Batching to Minimize Patient Turnaround Time
by: Novak, Antonin, et al.
Published: (2025)
by: Novak, Antonin, et al.
Published: (2025)
Understanding GNNs for Boolean Satisfiability through Approximation Algorithms
by: Hůla, Jan, et al.
Published: (2024)
by: Hůla, Jan, et al.
Published: (2024)
Two Pareto Optimum-based Heuristic Algorithms for Minimizing Tardiness and Late Jobs in the Single Machine Flowshop Problem
by: Gradwohl, Matthew, et al.
Published: (2024)
by: Gradwohl, Matthew, et al.
Published: (2024)
Parameterized Complexity of Scheduling Problems in Robotic Process Automation
by: Dvořák, Michal, et al.
Published: (2026)
by: Dvořák, Michal, et al.
Published: (2026)
Discovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling
by: Çetinkaya, İbrahim Oğuz, et al.
Published: (2025)
by: Çetinkaya, İbrahim Oğuz, et al.
Published: (2025)
Geometric Reasoning in the Embedding Space
by: Hůla, Jan, et al.
Published: (2025)
by: Hůla, Jan, et al.
Published: (2025)
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation
by: Song, Zhao, et al.
Published: (2023)
by: Song, Zhao, et al.
Published: (2023)
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
by: Huang, Weimin, et al.
Published: (2026)
by: Huang, Weimin, et al.
Published: (2026)
Neural Approaches to SAT Solving: Design Choices and Interpretability
by: Mojžíšek, David, et al.
Published: (2025)
by: Mojžíšek, David, et al.
Published: (2025)
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
by: Smit, Igor G., et al.
Published: (2024)
by: Smit, Igor G., et al.
Published: (2024)
Adaptive Sharpness-Aware Minimization with a Polyak-type Step size: A Theory-Grounded Scheduler
by: Oikonomou, Dimitris, et al.
Published: (2026)
by: Oikonomou, Dimitris, et al.
Published: (2026)
Exact and Heuristic Algorithms for Constrained Biclustering
by: Sudoso, Antonio M.
Published: (2025)
by: Sudoso, Antonio M.
Published: (2025)
Heuristic Optimal Transport in Branching Networks
by: Andrecut, M.
Published: (2023)
by: Andrecut, M.
Published: (2023)
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
by: Li, Sirui, et al.
Published: (2025)
by: Li, Sirui, et al.
Published: (2025)
Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity
by: Umeda, Hikaru, et al.
Published: (2025)
by: Umeda, Hikaru, et al.
Published: (2025)
All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformers
by: Hijazi, Amira, et al.
Published: (2024)
by: Hijazi, Amira, et al.
Published: (2024)
Achieving $ε^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions
by: Hamza, Ishaq, et al.
Published: (2026)
by: Hamza, Ishaq, et al.
Published: (2026)
Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging
by: Meterez, Alexandru, et al.
Published: (2026)
by: Meterez, Alexandru, et al.
Published: (2026)
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
by: Chen, Xi, et al.
Published: (2024)
by: Chen, Xi, et al.
Published: (2024)
Sharpness-Aware Minimization Can Hallucinate Minimizers
by: Park, Chanwoong, et al.
Published: (2025)
by: Park, Chanwoong, et al.
Published: (2025)
Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines
by: Piccialli, Veronica, et al.
Published: (2023)
by: Piccialli, Veronica, et al.
Published: (2023)
The Data-Driven Censored Newsvendor Problem
by: Hssaine, Chamsi, et al.
Published: (2024)
by: Hssaine, Chamsi, et al.
Published: (2024)
Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
by: Petrov, Egor, et al.
Published: (2025)
by: Petrov, Egor, et al.
Published: (2025)
Machine Learning for Inverse Problems and Data Assimilation
by: Bach, Eviatar, et al.
Published: (2024)
by: Bach, Eviatar, et al.
Published: (2024)
Less is More: Convergence Benefits of Fewer Data Weight Updates over Longer Horizon
by: Das, Rudrajit, et al.
Published: (2026)
by: Das, Rudrajit, et al.
Published: (2026)
Diffusion Model for Data-Driven Black-Box Optimization
by: Li, Zihao, et al.
Published: (2024)
by: Li, Zihao, et al.
Published: (2024)
In-Context Learning for Data-Driven Censored Inventory Control
by: Mukherjee, Sohom, et al.
Published: (2026)
by: Mukherjee, Sohom, et al.
Published: (2026)
Data-Driven Performance Guarantees for Classical and Learned Optimizers
by: Sambharya, Rajiv, et al.
Published: (2024)
by: Sambharya, Rajiv, et al.
Published: (2024)
Data-Driven Discovery of PDEs via the Adjoint Method
by: Sadr, Mohsen, et al.
Published: (2024)
by: Sadr, Mohsen, et al.
Published: (2024)
Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis
by: Davidovich, Orit, et al.
Published: (2025)
by: Davidovich, Orit, et al.
Published: (2025)
Optimal Data Splitting in Distributed Optimization for Machine Learning
by: Medyakov, Daniil, et al.
Published: (2024)
by: Medyakov, Daniil, et al.
Published: (2024)
Design and Scheduling of an AI-based Queueing System
by: Lee, Jiung, et al.
Published: (2024)
by: Lee, Jiung, et al.
Published: (2024)
Learning Rate Schedules in the Presence of Distribution Shift
by: Fahrbach, Matthew, et al.
Published: (2023)
by: Fahrbach, Matthew, et al.
Published: (2023)
A Novel Hybrid Heuristic-Reinforcement Learning Optimization Approach for a Class of Railcar Shunting Problems
by: Zhao, Ruonan, et al.
Published: (2026)
by: Zhao, Ruonan, et al.
Published: (2026)
Data-Driven Stochastic AC-OPF using Gaussian Processes
by: Mitrovic, Mile
Published: (2024)
by: Mitrovic, Mile
Published: (2024)
Cautious Weight Decay
by: Chen, Lizhang, et al.
Published: (2025)
by: Chen, Lizhang, et al.
Published: (2025)
From Data to Uncertainty Sets: a Machine Learning Approach
by: Bertsimas, Dimitris, et al.
Published: (2025)
by: Bertsimas, Dimitris, et al.
Published: (2025)
Similar Items
-
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
by: Bouška, Michal, et al.
Published: (2024) -
Urgent Samples in Clinical Laboratories: Stochastic Batching to Minimize Patient Turnaround Time
by: Novak, Antonin, et al.
Published: (2025) -
Understanding GNNs for Boolean Satisfiability through Approximation Algorithms
by: Hůla, Jan, et al.
Published: (2024) -
Two Pareto Optimum-based Heuristic Algorithms for Minimizing Tardiness and Late Jobs in the Single Machine Flowshop Problem
by: Gradwohl, Matthew, et al.
Published: (2024) -
Parameterized Complexity of Scheduling Problems in Robotic Process Automation
by: Dvořák, Michal, et al.
Published: (2026)