The Probabilistic Orienteering Problem (POP) is a variant of the orienteering problem where customers are available with a certain probability. In a previous work, we approximated its objective function value by using a Monte Carlo Sampling method. A heuristic speed-up criterion is considered in the objective function evaluator. In this work we study systematically the impact of the heuristic speed-up criterion in terms of precision and speed on the Monte Carlo evaluator, as well as the performance of a POP solver we propose, based on the embedding of the Monte Carlo evaluator into a Random Restart Local Search metaheuristic algorithm.
A metaheuristic algorithm for the probabilistic orienteering problem / Chou, X.; Gambardella, L. M.; Montemanni, R.. - (2019), pp. 30-34. (Intervento presentato al convegno 2nd International Conference on Machine Learning and Machine Intelligence, MLMI 2019 tenutosi a Shenzhen nel 2019) [10.1145/3366750.3366761].
A metaheuristic algorithm for the probabilistic orienteering problem
Montemanni R.
2019
Abstract
The Probabilistic Orienteering Problem (POP) is a variant of the orienteering problem where customers are available with a certain probability. In a previous work, we approximated its objective function value by using a Monte Carlo Sampling method. A heuristic speed-up criterion is considered in the objective function evaluator. In this work we study systematically the impact of the heuristic speed-up criterion in terms of precision and speed on the Monte Carlo evaluator, as well as the performance of a POP solver we propose, based on the embedding of the Monte Carlo evaluator into a Random Restart Local Search metaheuristic algorithm.
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