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Large Language Models (LLMs) have shown remarkable capabilities across various domains, but their potential for solving combinatorial optimization problems remains largely unexplored.
The complexity of flowshop and jobshop scheduling
Garey, M. R., Johnson, D. S., and Sethi, R · 1976
Earlier work this paper cites.
Benchmarks for basic scheduling problems
Taillard, E · 1993
Earlier work this paper cites.
Benchmarks for shop scheduling problems
Demirkol, E., Mehta, S., and Uzsoy, R · 1998
Earlier work this paper cites.
An advanced tabu search algorithm for the job shop problem
Nowicki, E. and Smutnicki, C · 2005
Earlier work this paper cites.
Combining constraint programming and local search for job-shop scheduling
Beck, J. C., Feng, T. K., and Watson, J.-P · 2010
Earlier work this paper cites.
Comparison of dispatching rules in job-shop scheduling problem using simulation: A case study
Chaudhry, S. A. and Khan, S · 2015
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Multiple priority dispatching rules for the job shop scheduling problem
Zahmani, M. H., Atmani, B., Bekrar, A., and Aissani, N · 2015
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Job shop scheduling problem and solution algorithms: A review
Cebi, C., Atac, E., and Sahingoz, O. K · 2020
Earlier work this paper cites.
Learning to dispatch for job shop scheduling via deep reinforcement learning
Zhang, C., Song, W., Cao, Z., Zhang, J., Tan, P. S., and Xu, C · 2020
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Cited alongside, same era.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Huang, W., Abbeel, P., Pathak, D., and Mordatch, I · 2022
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Large language models still can’t plan: A benchmark for llms on planning and reasoning about change
Valmeekam, K., Olmo, A., Sreedharan, S., and Kambhampati, S · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2022
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A rank stabilization scaling factor for fine-tuning with lora, 2023
Kalajdzievski, D · 2023
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2023
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Large language models for mathematical reasoning: Progresses and challenges
Ahn, J., Verma, R., Lou, R., Liu, D., Zhang, R., and Yin, W · 2024
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Llama 3 model card, 2024
AI, M · 2024
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Self-labeling the job shop scheduling problem
Corsini, A., Porrello, A., Calderara, S., and Dell’Amico, M · 2024
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Gpu-benchmarks-on-llm-inference
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Job shop scheduling via deep reinforcement learning: a sequence to sequence approach
Bonetta, G., Zago, D., Cancelliere, R., and Grosso, A · 2023
Cited alongside, same era.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2023
Cited alongside, same era.
llama.cpp: Llm inference in c/c++
Gerganov, G · 2023
Cited alongside, same era.
Exploring the potential of large language models (llms) in learning on graphs, 2024a
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., and Tang, J
Cited in the paper.
Exploring the potential of large language models (llms) in learning on graphs, 2024b
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., and Tang, J
Cited in the paper.
Dai, X · 2024
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Large language models for mathematicians, 2024
Frieder, S., Berner, J., Petersen, P., and Lukasiewicz, T · 2024
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Graphtext: Graph learning in text space, 2024
Zhao, J., Zhuo, L., Shen, Y., Qu, M., Liu, K., Bronstein, M. M., Zhu, Z., and Tang, J · 2024
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