Code tested and developed with Python 3.11.5 on macOS 12.7.1 and macOS 13.6.
This code repository includes various approachesto solve the stochastic orienteering problem, datasets to compare their performance, as well as scripts to generate and visualize data.
- StochasticOrienteeringMILP: solves problem using the MILP approach described in https://link.springer.com/content/pdf/10.1007/978-3-642-41575-3_30.pdf. To run this code you need a license for Gurobi. If you do not have it, replace the line "SOLVER" in the configuration files with CBC. It will use the CBC solver (which is much slower, but free).
- OrienteeringGraph: helper class modeling an instance of the orienteering problem.
- MCTS_StochasticOrienteering_TASE: code for results presented in TASE submissions (extends CASE 2022 paper).