Abstract
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.
Highlights
INTRODUCTIONEach city has its own tourist attractions, either natural beauty such as lakes and mountains, or cultural places such as museums and historical locations
The Tourist Trip Design Problem is a variant of a route-planning problem for tourists interested in multiple points of interest
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
Summary
Each city has its own tourist attractions, either natural beauty such as lakes and mountains, or cultural places such as museums and historical locations. The Tourist Trip Design Problem (TTDP) is a variant of a route-planning problem for tourists interested in visiting multiple points of interest (POI). The objective is to select a subset of POIs to visit within the length of the stay, a given time b, in such a way that the satisfaction score of the tourist is maximized, while the total time spent between attractions and the total travel time is minimized. For POIs with deterministic availability, a simple formulation of the TTDP is proved to be identical with the Orienteering Problem (OP), (Vansteenwegen, Souffriau, Vanden Berghe, & Van Oudheusden, 2009), where a route with maximum score is determined for a subset of locations with fixed depot and destination, limited by the time budget. Since finding the best route for a deterministic TTDP with large number of POIs is already a rather time-consuming problem, how practical could it be to consider such a stochastic TTDP? In this paper we aim at demonstrating that the existing model of the Probabilistic Orienteering Problem (POP) Angelelli, Archetti, Filippi, & Vindigni (2017), fits the TTDP well, and we introduce a heuristic method to solve the problem based on Monte Carlo Sampling techniques [3] (Chou, Gambardella, & Montemanni, 2018)
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