Monte Carlo sampling for the tourist trip design problem

  • Xiaochen Chou IDSIA - Dalle Molle Institute for Artificial Intelligence (USI-SUPSI), Switzerland
  • Luca Maria Gambardella IDSIA - Dalle Molle Institute for Artificial Intelligence (USI-SUPSI), Switzerland
  • Roberto Montemanni Dept. of Engineering Sciences and Methods, University of Modena and Reggio Emilia, Italy
Keywords: The Tourist Trip Design Problem, Probabilistic Orienteering Problem, Monte Carlo Sampling, Combinatorial Optimization


Introduction: The Tourist Trip Design Problem is a variant of a route-planning problem for tourists interested in multiple points of interest. Each point of interest has different availability, and a certain satisfaction score can be achieved when it is visited.

Objectives: The objective is to select a subset of points of interests to visit within a given time budget, in such a way that the satisfaction score of the tourist is maximized and the total travel time is minimized.

Methods: In our proposed model, the calculation of the availability of a POI is based on the waiting time and / or the weather forecast. However, research shows that most tourists prefer to travel within a crowded and limited area of very attractive POIs for safety reasons and because they feel more in control.

Results: In this work we demonstrate that the existing model of the Probabilistic Orienteering Problem fits a probabilistic variant of this problem and that Monte Carlo Sampling techniques can be used inside a heurist solver to efficiently provide solutions.

Conclusions: In this work we demonstrate the existing model of the Probabilistic Orienteering Problem fits the stochastic Tourist Trip Design Problem. We proposed a way to solve the problem by using Monte Carlo Sampling techniques inside a heuristic solver and discussed several possible improvements on the model. Further extension of the model will be developed for solving more practical problems.


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