> For the complete documentation index, see [llms.txt](https://paulorocosta.gitbook.io/ai4tsp-competition/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://paulorocosta.gitbook.io/ai4tsp-competition/tracks/track-1-online-supervised-learning-surrogates.md).

# Track 1: Online Supervised Learning (surrogates)

Problem: time-dependent orienteering problem with stochastic weights and time windows (TD-OPSWTW) \[1]. Given one instance, previously tried routes, and the reward for those routes, the goal is to learn a model that can predict the reward for a new route. Then an optimizer finds the route that gives the best reward according to that model, and that route is then evaluated, giving a new data point. Then the model is updated, and this iterative procedure continues for a fixed number of steps.

[\[1\]](https://www.sciencedirect.com/science/article/pii/S037722171630368X) C Verbeeck, Pieter Vansteenwegen, and E-H Aghezzaf. Solving the stochastic time-dependent orienteering problem with time windows. European Journal of Operational Research, 255(3):699–718, 2016.