Online TSP and Online Dial-a-Ride with Predictions

We study online routing problems with predictions, inspired by recent exciting results emerged from the area of learning-augmented algorithms. A learning-augmented online algorithm, which incorporates predictions into a black-box manner to outperform existing algorithms if the predictions are accurate while otherwise maintaining theoretical guarantees, is a popular framework for overcoming pessimistic worst-case competitive analysis. In this paper, we particularly investigate the classical online traveling salesman problem (OLTSP) and online dial-a-ride problem (OLDARP), where future requests are augmented with predictions. Unlike the prediction models in other previous studies, each actual request in the OLTSP and OLDARP is associated with its arrival time and position, which, as imagined, leads to a more complicated situation. Our main result is to study different prediction models and design algorithms to improve the best-known results in the different settings. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by National Science Council [Grants NSTC110-2221-E-007-106-MY3, NSTC111-2221-E-007-052-MY3].