# Solving Stochastic Orienteering Problems with Chance Constraints Using Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/2409.03170 · HTML: https://mlsanity.com/paper/2409.03170/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2024.

## Built on by (most cited first, 5 of 5)

- [Solving Stochastic Orienteering Problems with Chance Constraints Using a GNN Powered Monte Carlo Tree Search](https://mlsanity.com/paper/2409.04653/) (2024)
- [Leveraging LLMs for Mission Planning in Precision Agriculture](https://mlsanity.com/paper/2506.10093/) (2025)
- [N(CO)2: Neural Combinatorial Optimization with Chance Constraints to Solve Stochastic Orienteering](https://mlsanity.com/paper/2606.18514/) (2026)
- [As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture](https://mlsanity.com/paper/2606.18519/) (2026)
- [Learning-Assisted Congestion-Aware Route Scheduling for Semiconductor Fab Material Control Systems](https://mlsanity.com/paper/2608.30520/) (2026)

## Abstract (arXiv)

We present a new Monte Carlo Tree Search (MCTS) algorithm to solve the stochastic orienteering problem with chance constraints, i.e., a version of the problem where travel costs are random, and one is assigned a bound on the tolerable probability of exceeding the budget. The algorithm we present is online and anytime, i.e., it alternates planning and execution, and the quality of the solution it produces increases as the allowed computational time increases. Differently from most former MCTS algorithms, for each action available in a state the algorithm maintains estimates of both its value and the probability that its execution will eventually result in a violation of the chance constraint. Then, at action selection time, our proposed solution prunes away trajectories that are estimated to violate the failure probability. Extensive simulation results show that this approach can quickly produce high-quality solutions and is competitive with the optimal but time-consuming solution.