mlsanityData to 7 Oct 2026

LLMs can Schedule

arXiv 2408.06993 ↗First posted 13 Aug 2024cs.AIcited by 11

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Abstract (arXiv)

The job shop scheduling problem (JSSP) remains a significant hurdle in optimizing production processes. This challenge involves efficiently allocating jobs to a limited number of machines while minimizing factors like total processing time or job delays. While recent advancements in artificial intelligence have yielded promising solutions, such as reinforcement learning and graph neural networks, this paper explores the potential of Large Language Models (LLMs) for JSSP. We introduce the very first supervised 120k dataset specifically designed to train LLMs for JSSP. Surprisingly, our findings demonstrate that LLM-based scheduling can achieve performance comparable to other neural approaches. Furthermore, we propose a sampling method that enhances the effectiveness of LLMs in tackling JSSP.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 13 Aug 2024.

Builds on

6 of 16 references.

References · 6

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  3. 2023Large Language Models as Optimizerscited by 830
  4. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 139
  5. 2024Self-Labeling the Job Shop Scheduling Problemcited by 23
  6. 2023Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approachcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 4.

11 papers.

  1. 2025Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Designcited by 100
  2. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 9
  3. 2024Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimizationcited by 8
  4. 2025Generalizable Heuristic Generation Through LLMs with Meta-Optimizationcited by 7
  5. 2025Large Language Models in Operations Research: Methods, Applications, and Challengescited by 4
  6. 2025ALAS: Transactional and Dynamic Multi-Agent LLM Planningcited by 3
  7. 2025Teaching LLMs to Think Mathematically: A Critical Study of Decision-Making via Optimizationcited by 1
  8. 2026Aligning LLMs with Graph Neural Solvers for Combinatorial Optimizationcited by 1
  9. 2026HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimizationcited by 0
  10. 2026GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimizationcited by 0
  11. 2026Large Language Models as Optimizers: A Survey of Direct vs. Tool-Augmented Approaches and Their Performance Frontierscited by 0