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Reseach Article

Generating an Optimal Tour Plan with Optimization

by Bhagya Rathnayake, Dharshana Kasthurirathna
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 38
Year of Publication: 2022
Authors: Bhagya Rathnayake, Dharshana Kasthurirathna
10.5120/ijca2022922473

Bhagya Rathnayake, Dharshana Kasthurirathna . Generating an Optimal Tour Plan with Optimization. International Journal of Computer Applications. 184, 38 ( Dec 2022), 31-39. DOI=10.5120/ijca2022922473

@article{ 10.5120/ijca2022922473,
author = { Bhagya Rathnayake, Dharshana Kasthurirathna },
title = { Generating an Optimal Tour Plan with Optimization },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2022 },
volume = { 184 },
number = { 38 },
month = { Dec },
year = { 2022 },
issn = { 0975-8887 },
pages = { 31-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number38/32565-2022922473/ },
doi = { 10.5120/ijca2022922473 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:29.780358+05:30
%A Bhagya Rathnayake
%A Dharshana Kasthurirathna
%T Generating an Optimal Tour Plan with Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 38
%P 31-39
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tourism is an industry that has widespread acrossthe globe. It was built around the natural desire of humansto travel and to facilitate their needs. With the evolution ofinformation and technology, the tourism industry is expanding,popularizing lots of new travel destinations among tourists. Thesimple tour plans made by tourists earlier are no longer goingto work as the number of travel destination choices availablein any country has gone high with the information availability.The higher the number of choices is the higher it goes withthe complexity of generating tour plans that returns satisfactorytour experiences. This research paper discusses the ability to usethe concepts of optimization in machine learning to generate anoptimal tour plan by evaluating the tourist’s interests. The paperexpresses how the 0-1 knapsack algorithm can be improved andused against a data set in a heuristic approach to generate anoptimal tour plan that can minimize the waste of time and moneyof the tourist while maximizing the relevance of the segmentsincluded in the tour plan for the tourist.

References
  1. K. Codes, “Genetic algorithms explained by example,” in youtube, Jul2020.
  2. “Genetic algorithm from scratch in python (tutorial with code),”in youtube, Jul 2020..
  3. D. Ajantha, J. Vijay, and R. Sridhar, “A user-location vector basedapproach for personalised tourism and travel recommendation,” in 2017International Conference on Big Data Analytics and ComputationalIntelligence (ICBDAC), 2017, pp. 440–446.
  4. W. Chang and L. Ma, “Personalized e-tourism attraction recommendation based on context,” in 2013 10th International Conference on ServiceSystems and Service Management, 2013, pp. 674–679.
  5. V. Savchuk and V. Pasichnyk, “Modelling decision-making processes inthe field of individual tourism,” in 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and InformationTechnologies (CSIT), vol. 1, 2018, pp. 223–226.
  6. M. A. Pavel, M. Rana, A. A. Roman, Y. Hassan, and R. Khan, “Androidapplication for tourism planning in bangladesh,” in 2021 IEEE 19thStudent Conference on Research and Development (SCOReD), 2021,pp. 157–162.
  7. Z. Mu, C. Shan, L. Jing, and F. Lei, “Design of the tourism-informationservice-oriented collaborative filtering recommendation algorithm,” in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), vol. 13, 2010, pp. V13–361–V13–365.
  8. T. Hasuike, H. Tsubaki, H. Katagiri, and H. Tsuda, “Personal tour planning incorporating standard tour routes and tourist satisfaction,” in 2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA), 2013, pp. 143–148.
  9. F. M. Mahmood and Z. A. Bin Abdul Salam, “A conceptual framework for personalized location-based services (lbs) tourism mobile application leveraging semantic web to enhance tourism experience,” in 2013 3rd IEEE International Advance Computing Conference (IACC), 2013, pp. 287–291.
  10. Z. F. Jailani, P. Verweij, J. T. Van Der Wal, and R. Van Lammeren, “A machine learning approach to study tourist interests and predict tourism demand on bonaire island from social media data - note: This research is based on the internship research report that has already uploaded to www.dcbd.nl,” in 2021 13th International Conference on Information & Communication Technology and System (ICTS), 2021, pp. 173–178.
  11. R. Nagar, Y. Singh, V. Jaglan, and Meenakshi, “A review on machine learning applications in medical tourism,” in 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT), 2021, pp. 208–215.
  12. T. Ghani, N. Jahan, S. H. Ridoy, A. T. Khan, S. Khan, and M. M. Khan, “Amar bangladesh - a machine learning based smart tourist guidance system,” in 2018 2nd International Conference on Electronics, Materials Engineering &Nano-Technology (IEMENTech), 2018, pp. 1–5.
  13. S. L. Government, “Stats available at srilanka tourism development authority,” in SLTDA, Jul 2020.
Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Optimization Genetic Algorithm Tour Planning.