Monte Carlo sampling for the tourist trip design problem
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 s...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN PT |
| Publicado: |
Instituto Politécnico de Viseu
2019
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/e2c45fe1d9574ef4a76a75e1788aa71d |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:e2c45fe1d9574ef4a76a75e1788aa71d |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:e2c45fe1d9574ef4a76a75e1788aa71d2021-12-02T15:56:24ZMonte Carlo sampling for the tourist trip design problem10.29352/mill0210.09.002590873-30151647-662Xhttps://doaj.org/article/e2c45fe1d9574ef4a76a75e1788aa71d2019-09-01T00:00:00Zhttps://revistas.rcaap.pt/millenium/article/view/18633https://doaj.org/toc/0873-3015https://doaj.org/toc/1647-662X 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. Xiaochen ChouLuca Maria GambardellaRoberto MontemanniInstituto Politécnico de ViseuarticleThe Tourist Trip Design ProblemProbabilistic Orienteering ProblemMonte Carlo SamplingCombinatorial OptimizationSpecial aspects of educationLC8-6691Public aspects of medicineRA1-1270ENPTMillenium, Vol 2, Iss 10 (2019) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN PT |
| topic |
The Tourist Trip Design Problem Probabilistic Orienteering Problem Monte Carlo Sampling Combinatorial Optimization Special aspects of education LC8-6691 Public aspects of medicine RA1-1270 |
| spellingShingle |
The Tourist Trip Design Problem Probabilistic Orienteering Problem Monte Carlo Sampling Combinatorial Optimization Special aspects of education LC8-6691 Public aspects of medicine RA1-1270 Xiaochen Chou Luca Maria Gambardella Roberto Montemanni Monte Carlo sampling for the tourist trip design problem |
| description |
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.
|
| format |
article |
| author |
Xiaochen Chou Luca Maria Gambardella Roberto Montemanni |
| author_facet |
Xiaochen Chou Luca Maria Gambardella Roberto Montemanni |
| author_sort |
Xiaochen Chou |
| title |
Monte Carlo sampling for the tourist trip design problem |
| title_short |
Monte Carlo sampling for the tourist trip design problem |
| title_full |
Monte Carlo sampling for the tourist trip design problem |
| title_fullStr |
Monte Carlo sampling for the tourist trip design problem |
| title_full_unstemmed |
Monte Carlo sampling for the tourist trip design problem |
| title_sort |
monte carlo sampling for the tourist trip design problem |
| publisher |
Instituto Politécnico de Viseu |
| publishDate |
2019 |
| url |
https://doaj.org/article/e2c45fe1d9574ef4a76a75e1788aa71d |
| work_keys_str_mv |
AT xiaochenchou montecarlosamplingforthetouristtripdesignproblem AT lucamariagambardella montecarlosamplingforthetouristtripdesignproblem AT robertomontemanni montecarlosamplingforthetouristtripdesignproblem |
| _version_ |
1718385422450556928 |