USING FLOYD’S ALGORITHM TO OPTIMIZE TOURISM ROUTES ON LOMBOK ISLAND
Keywords:
optimal route, digraph, tourism network, Floyd’s algorithm, favorites destinationDOI:
https://doi.org/10.17654/0972096025006Abstract
Lombok Island is one of the superpriority areas of tourism destinations which is of competitive advantage and a leading sector of the economy of West Nusa Tenggara (NTB) Province, Indonesia. This research aims to determine the optimal tourism transportation route on Lombok Island as a part of the development of its tourism system on Lombok Island. The Lombok Island Tourism Network (Jaringan Pariwisata Pulau Lombok, JP2L) in this study was built by 55 tourist destinations. Furthermore, JP2L is represented by a digraph with the set V representing the set of tourist destinations and the set E representing the road section connecting each tourist destination pair. The application of Floyd’s algorithm to JP2L provides an optimal tourist route on the island of Lombok along with all possible destinations to be passed on the selected travel route. Finally, based on whether or not a tourist location is visited from and to a tourist location (destination among favorites), this study produces recommendations for destinations between favorites, both from the destination tourist session and the original tourist destination.
Received: January 5, 2025
Accepted: March 21, 2025
References
ddddddd
J. Zhang, H. Ebbers and C. Zhou, Flows of tourists, commodities and investment: the case of China, Tourism Economics, Physica-Verlag HD, 2011, pp. 43-63. doi: 10.1007/978-3-7908-2725-5_4.
NTB Tourism Office, Government Tourism Office, Accessed: February 5, 2022. [Online]. Available: https://data.ntbprov.go.id/group/dinas-pariwisata.
T. Nemoto, N. Niitsuma and N. Kamamichi, Optimal routing with resource assignment for traveling among farms, IFAC-PapersOnLine, Elsevier B.V., 2022, pp. 271-276. doi: 10.1016/j.ifacol.2022.09.358.
Y. Yamasaki and N. Noguchi, Optimal route planning and a turning method for an electric vehicle robot in an irregularly shaped vineyard, IFAC-PapersOnLine, Elsevier B.V., 2022, pp. 1-5. doi: 10.1016/j.ifacol.2022.11.105.
T. Xu, B. Ran and Y. Cui, Dynamic user optimal route choice problem on a signalized transportation network, Transportation Engineering 13 (2023), 100153. doi: 10.1016/j.treng.2022.100153.
Y. C. Hung, H. PakHai Lok and G. Michailidis, Optimal routing for electric vehicle charging systems with stochastic demand: a heavy traffic approximation approach, Eur. J. Oper. Res. 299(2) (2022), 526-541.
doi: 10.1016/j.ejor.2021.06.058.
A. Viloria, N. A. L. Zelaya and N. Varela, Search for optimal routes on roads applying metaheuristic algorithms, Procedia Computer Science 175 (2020), 447-452. doi: 10.1016/j.procs.2020.07.063.
D. Ingole, G. Mariotte and L. Leclercq, Minimizing network-wide emissions by optimal routing through inner-city gating, Transp. Res. D Transp. Environ. 86 (2020), 102411. doi: 10.1016/j.trd.2020.102411.
S. Beczkowska, The method of optimal route selection in road transport of dangerous goods, Transportation Research Procedia 40 (2019), 1252-1259. doi: 10.1016/j.trpro.2019.07.174.
F. AlRukaibi, D. Alrukaibi, S. Alkheder, S. Alojaiman and T. Sayed, Optimal route risk-based algorithm for hazardous material transport in Kuwait, J. Loss Prev. Process. Ind. 52 (2018), 40-53. doi: 10.1016/j.jlp.2018.01.012.
N. Meenakshi, V. Pandimurugan, L. Sathishkumar and Chinnasamy, Optimal routing methodology to enhance the life time of sensor network, Materials Today: Proceedings, 46 (2021), 5894-5900. doi: 10.1016/j.matpr.2021.02.752.
X. Z. Wang, The comparison of three algorithms in shortest path issue, Journal of Physics: Conference Series 1087 (2018), 022011.
doi: 10.1088/1742-6596/1087/2/022011.
P. Grabusts, J. Musatovs and V. Golenkov, The application of simulated annealing method for optimal route detection between objects, Procedia Computer Science 149 (2019), 95-101. doi: 10.1016/j.procs.2019.01.112.
M. A. Mohammed, M. K. Abd Ghani, R. I. Hamed, S. A. Mostafa, M. S. Ahmad and D. A. Ibrahim, Solving vehicle routing problem by using improved genetic algorithm for optimal solution, J. Comput. Sci. 21 (2017), 255-262. doi: 10.1016/j.jocs.2017.04.003.
Y. Huang, C. Liang and Y. Yang, The optimum route problem by genetic algorithm for loading/unloading of yard crane, Comput. Ind. Eng. 56(3) (2009), 993-1001. doi: 10.1016/j.cie.2008.09.035.
S. Maity, A. Roy and M. Maiti, A modified genetic algorithm for solving uncertain constrained solid travelling salesman problems, Comput. Ind. Eng. 83 (2015), 273-296. doi: 10.1016/j.cie.2015.02.023.
D. Baeza, C. F. Ihle and J. M. Ortiz, A comparison between ACO and Dijkstra algorithms for optimal ore concentrate pipeline routing, J. Clean Prod. 144 (2017), 149-160. doi: 10.1016/j.jclepro.2016.12.084.
P. Venkataram, S. Ghosal and B. P. V. Kumar, Neural network based optimal routing algorithm for communication networks. [Online] Available: www.elsevier.com/locate/neunet.
T. Zhou, Deep learning models for route planning in road networks, Thesis, 2018.
E. O. Abubakar, O. Idoko and O. S. Ocholi, Efficient tour planning for tourist sites visitation in Lokoja, Nigeria: a multi-scenario analysis using GIS, Journal of Geographic Information System 9(1) (2017), 59-81. doi: 10.4236/jgis.2017.91005.
S. Bahri, L. Awalushaumi and N. A. Robbaniyyah, Fuzzy wavelet dynamic neural network model for modeling the number of tourist visits to west Nusatenggara province, IJCMEM 12(1) (2024), 1-8.
N. Deo, Graph Theory with Applications to Engineering and Computer Science, Dover Publications, Inc., New York, 2016.
D. Jungnickel, Graphs, Networks and Algorithms, 4th ed., Springer-Verlag, Berlin, Vol. 5, 2013.
J. R. Evans and E. Minieka, Optimization Algorithms for Networks and Graphs, 2nd ed., Marcel Dekker, Inc., New York, 1992.
Appendices 1-4
Downloads
Published
Issue
Section
License
Copyright (c) 2025 PUSHPA PUBLISHING HOUSE, PRAYAGRAJ, INDIA

This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Licensing Terms:
This work is published by Pushpa Publishing House and is subject to the following conditions:
Attribution: You must credit Pushpa Publishing House as the original publisher. Include the publication title and author(s) if applicable.
No Derivatives: Modifying the work or creating derivative works is not permitted without prior written permission.
For more information or permissions beyond the scope of this license, contact Pushpa Publishing House.






Google h-index: