ABSTRACT
In this paper, an algorithm for making travel itinerary using traveling salesman problem (TSP) and k-means clustering technique is proposed. We employ the algorithm to develop a web based application that can help travelers to plan their travel itinerary. The developed application should be able to provide an optimal itinerary recommendation in terms of distance and travel time. We use initial assumption that the traveler has determined all the tourist destinations he/she wants to visit and also the number of days he/she will stay in the region. Our approach consists of two steps, macro grouping using k-means and micro tour arrangement using TSP. Yogyakarta city, one of the tourist city in Indonesia, is used as an example to illustrate how the proposed algorithm can help travelers make their itinerary. This approach works well in small to medium number points of interest. However, the application still need many improvements such as to make it run faster and to handle the additional constraints that exist when creating an itinerary.
- Hsu, F. C. and Chen, P. 2000. Interactive genetic algorithms for a travel itinerary planning problem. TSP, 1, 13.Google Scholar
- Russell, S. and Norvig, P. 1995. Artificial Intelligence A Modern Approach. Prentice-Hall, Egnlewood Cliffs, 25, 27. Google ScholarDigital Library
- Hoffman, K. L., Padberg, M., and Rinaldi, G. 2013. Traveling salesman problem. In Encyclopedia of operations research and management science (pp. 1573-1578). Springer US.Google Scholar
- Cook, W. 2007. History of the TSP. http://www.math.uwaterloo.ca/tsp/history/index.html.Google Scholar
- Moon, C., Kim, J., Choi, G., and Seo, Y. 2002. An efficient genetic algorithm for the traveling salesman problem with precedence constraints. European Journal of Operational Research, 140(3), 606-617.Google ScholarCross Ref
- Razali, N. M. and Geraghty, J. 2011. Genetic algorithm performance with different selection strategies in solving TSP. In Proceedings of the world congress on engineering (Vol. 2, pp. 1134-1139).Google Scholar
- Dorigo, M. and Gambardella, L. M. 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1), 53-66. Google ScholarDigital Library
- Chen, S. M. and Chien, C. Y. 2011. Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. Expert Systems with Applications, 38(12), 14439-14450. Google ScholarDigital Library
- Murty, M. N. and Devi, V. S. 2011. Pattern recognition: An algorithmic approach. Springer Science & Business Media.Google Scholar
- Abbaspour, R. A. and Samadzadegan, F. 2011. Time-dependent personal tour planning and scheduling in metropolises. In Expert Systems with Applications, 38 (2011) 12439--12452. Google ScholarDigital Library
- Rizzo, C. 2017. This App Will Help You Effortlessly Plan a Multi-city European Vacation. http://www.travelandleisure.com/travel-tips/mobile-apps/eightydays-app-plans-european-vacation.Google Scholar
- Hagen, K., Kramer, R., Hermkes, M., Schumann, B., and Mueller, P. 2005. Semantic matching and heuristic search for a dynamic tour guide. Information and Communication Technologies in Tourism 2005, pp.149-159.Google Scholar
- Statistik Kepariwisataan 2016. https://visitingjogja.com/10193/statistik-pariwisata-2016/.Google Scholar
- Kauffman, L. and Rousseeuw, P. J. 1990. Finding Group in Data: An Introduction to Cluster Analysis. Wiley, New York.Google Scholar
Index Terms
- A Development of Travel Itinerary Planning Application using Traveling Salesman Problem and K-Means Clustering Approach
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