Geração de Roteiros Turísticos Personalizados em Tempo Real através de Heurística GRASP Adaptada
Resumo
Planejar uma viagem turística é uma tarefa desafiadora, pois envolve conciliar os interesses pessoais do usuário com restrições financeiras, logísticas e categóricas. Esse problema é formalmente conhecido como o Problema do Turista e é frequentemente tratado como um problema de otimização. Métodos exatos frequentemente falham em encontrar soluções ótimas em tempo hábil para este problema, o que torna meta-heurísticas uma alternativa viável. Neste trabalho, propomos uma abordagem baseada em GRASP para solucionar o Problema do Turista. Nossa técnica se diferencia por considerar não apenas a utilidade dos locais visitados, mas também a proximidade geográfica, promovendo a exploração de áreas vizinhas. Além disso, nossa abordagem foi avaliada com dados reais da cidade do Rio de Janeiro, alcançando um desempenho superior a outras técnicas da literatura, com melhoria de até 60% em utilidade, e um baixo custo computacional, gerando rotas em menos de 1 segundo.
Palavras-chave:
Turismo, recomendação de roteiros, otimização
Referências
Aliano Filho, A. and Morabito, R. (2024). An effective approach for bi-objective multiperiod touristic itinerary planning. Expert Systems with Applications, 240, 122437.
Brito, J., Expósito-Márquez, A., and Moreno, J. A. (2017). A fuzzy grasp algorithm for solving a tourist trip design problem. In 2017 IEEE International Conference on Fuzzy Systems.
Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., et al. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study. Information Processing & Management, 58(3), 102481.
Cunha, W., Moreo, A., Esuli, A., Sebastiani, F., Rocha, L., and Gonçalves, M. A. (2025). A noise-oriented and redundancy-aware instance selection framework. ACM Transactions on Information Systems.
Exposito, A., Mancini, S., Brito, J., and Moreno, J. A. (2019). A fuzzy grasp for the tourist trip design with clustered points of interest. Expert Systems with Applications, 127, 210–227.
Feo, T. A. and Resende, M. G. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6, 109–133.
Friggstad, Z., Gollapudi, S., Kollias, K., Sarlos, T., Swamy, C., and Tomkins, A. (2018). Orienteering algorithms for generating travel itineraries. In Proceedings of the Conference on Information and Knowledge Management (CIKM).
Gavalas, D., Kasapakis, V., Konstantopoulos, C., Pantziou, G., and Vathis, O. (2017). Scenic route planning for tourists. Personal and Ubiquitous Computing.
Gavalas, D., Konstantopoulos, C., Mastakas, K., and Pantziou, G. (2014). A survey on algorithmic approaches for solving tourist trip design problems. Journal of Heuristics.
Ghobadi, F., Divsalar, A., Jandaghi, H., and Nozari, R. B. (2023). An integrated recommender system for multi-day tourist itinerary. Applied Soft Computing, 149, 110942.
Headspin (2023). Why measuring and optimizing response time is critical for applications success. [link]. Accessed: 2023-10-17.
Kotiloglu, S., Lappas, T., Pelechrinis, K., and Repoussis, P. (2017). Personalized multiperiod tour recommendations. Tourism Management, 62, 76–88.
Pang, B., Lee, L., et al. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135.
Ruiz-Meza, J., Brito, J., and Montoya-Torres, J. R. (2021). A grasp to solve the multiconstraints multi-modal team orienteering problem with time windows for groups with heterogeneous preferences. Computers & Industrial Engineering, 162, 107776.
Ruiz-Meza, J., Brito, J., and Montoya-Torres, J. R. (2022). A grasp-vnd algorithm to solve the multi-objective fuzzy and sustainable tourist trip design problem for groups. Applied Soft Computing, 131, 109716.
Ruiz-Meza, J. and Montoya-Torres, J. R. (2022). A systematic literature review for the tourist trip design problem: Extensions, solution techniques and future research lines. Operations Research Perspectives, 9, 100228.
Sarkar, J. L. and Majumder, A. (2021). A new point-of-interest approach based on multi-itinerary recommendation engine. Expert Systems with Applications, 181, 115026.
Talbi, E.-G. (2009). Metaheuristics: From Design to Implementation. John Wiley & Sons.
Vansteenwegen, P., Souffriau, W., Berghe, G. V., and Van Oudheusden, D. (2011). The city trip planner: An expert system for tourists. Expert Systems with Applications.
Brito, J., Expósito-Márquez, A., and Moreno, J. A. (2017). A fuzzy grasp algorithm for solving a tourist trip design problem. In 2017 IEEE International Conference on Fuzzy Systems.
Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., et al. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study. Information Processing & Management, 58(3), 102481.
Cunha, W., Moreo, A., Esuli, A., Sebastiani, F., Rocha, L., and Gonçalves, M. A. (2025). A noise-oriented and redundancy-aware instance selection framework. ACM Transactions on Information Systems.
Exposito, A., Mancini, S., Brito, J., and Moreno, J. A. (2019). A fuzzy grasp for the tourist trip design with clustered points of interest. Expert Systems with Applications, 127, 210–227.
Feo, T. A. and Resende, M. G. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6, 109–133.
Friggstad, Z., Gollapudi, S., Kollias, K., Sarlos, T., Swamy, C., and Tomkins, A. (2018). Orienteering algorithms for generating travel itineraries. In Proceedings of the Conference on Information and Knowledge Management (CIKM).
Gavalas, D., Kasapakis, V., Konstantopoulos, C., Pantziou, G., and Vathis, O. (2017). Scenic route planning for tourists. Personal and Ubiquitous Computing.
Gavalas, D., Konstantopoulos, C., Mastakas, K., and Pantziou, G. (2014). A survey on algorithmic approaches for solving tourist trip design problems. Journal of Heuristics.
Ghobadi, F., Divsalar, A., Jandaghi, H., and Nozari, R. B. (2023). An integrated recommender system for multi-day tourist itinerary. Applied Soft Computing, 149, 110942.
Headspin (2023). Why measuring and optimizing response time is critical for applications success. [link]. Accessed: 2023-10-17.
Kotiloglu, S., Lappas, T., Pelechrinis, K., and Repoussis, P. (2017). Personalized multiperiod tour recommendations. Tourism Management, 62, 76–88.
Pang, B., Lee, L., et al. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135.
Ruiz-Meza, J., Brito, J., and Montoya-Torres, J. R. (2021). A grasp to solve the multiconstraints multi-modal team orienteering problem with time windows for groups with heterogeneous preferences. Computers & Industrial Engineering, 162, 107776.
Ruiz-Meza, J., Brito, J., and Montoya-Torres, J. R. (2022). A grasp-vnd algorithm to solve the multi-objective fuzzy and sustainable tourist trip design problem for groups. Applied Soft Computing, 131, 109716.
Ruiz-Meza, J. and Montoya-Torres, J. R. (2022). A systematic literature review for the tourist trip design problem: Extensions, solution techniques and future research lines. Operations Research Perspectives, 9, 100228.
Sarkar, J. L. and Majumder, A. (2021). A new point-of-interest approach based on multi-itinerary recommendation engine. Expert Systems with Applications, 181, 115026.
Talbi, E.-G. (2009). Metaheuristics: From Design to Implementation. John Wiley & Sons.
Vansteenwegen, P., Souffriau, W., Berghe, G. V., and Van Oudheusden, D. (2011). The city trip planner: An expert system for tourists. Expert Systems with Applications.
Publicado
19/05/2025
Como Citar
FÉLIX, Lucas G. S.; CUNHA, Washington; XAVIER, Carolina Ribeiro; VAZ DE MELO, Pedro Olmo; GONÇALVES, Marcos André; ALMEIDA, Jussara Marques.
Geração de Roteiros Turísticos Personalizados em Tempo Real através de Heurística GRASP Adaptada. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 196-209.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2025.5884.
