# Online TSP with Predictions

arXiv: https://arxiv.org/abs/2206.15364 · HTML: https://mlsanity.com/paper/2206.15364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [The Primal-Dual method for Learning Augmented Algorithms](https://mlsanity.com/paper/2010.11632/) (2020)
- [Near-Optimal Bounds for Online Caching with Machine Learned Advice](https://mlsanity.com/paper/1910.12172/) (2019)
- [Secretary and Online Matching Problems with Machine Learned Advice](https://mlsanity.com/paper/2006.01026/) (2020)
- [Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms](https://mlsanity.com/paper/2010.11443/) (2020)
- [Online Computation with Untrusted Advice](https://mlsanity.com/paper/1905.05655/) (2019)
- [Secretaries with Advice](https://mlsanity.com/paper/2011.06726/) (2020)
- [Better and Simpler Learning-Augmented Online Caching](https://mlsanity.com/paper/2005.13716/) (2020)
- [Learning Augmented Energy Minimization via Speed Scaling](https://mlsanity.com/paper/2010.11629/) (2020)
- [Online Algorithms for Weighted Paging with Predictions](https://mlsanity.com/paper/2006.09509/) (2020)
- [Online Graph Algorithms with Predictions](https://mlsanity.com/paper/2112.11831/) (2021)
- [Revenue Optimization with Approximate Bid Predictions](https://mlsanity.com/paper/1706.04732/) (2017)
- [Customizing ML Predictions for Online Algorithms](https://mlsanity.com/paper/2205.08715/) (2022)
- [Queues with Small Advice](https://mlsanity.com/paper/2006.15463/) (2020)
- [Improved Bounds for Online Facility Location with Predictions](https://mlsanity.com/paper/2107.08277/) (2021)

## Built on by (most cited first, 1 of 1)

- [Asymptotically Robust Learning-Augmented Algorithms for Preemptive FIFO Buffer Management](https://mlsanity.com/paper/2604.26349/) (2026)

## Abstract (arXiv)

We initiate the study of online routing problems with predictions, inspired by recent exciting results in the area of learning-augmented algorithms. A learning-augmented online algorithm which incorporates predictions in a black-box manner to outperform existing algorithms if the predictions are accurate while otherwise maintaining theoretical guarantees even when the predictions are extremely erroneous is a popular framework for overcoming pessimistic worst-case competitive analysis. In this study, we particularly begin investigating the classical online traveling salesman problem (OLTSP), where future requests are augmented with predictions. Unlike the prediction models in other previous studies, each actual request in the OLTSP, associated with its arrival time and position, may not coincide with the predicted ones, which, as imagined, leads to a troublesome situation. Our main result is to study different prediction models and design algorithms to improve the best-known results in the different settings. Moreover, we generalize the proposed results to the online dial-a-ride problem.