Improving Veterinary Service Efficiency: Optimizing Home Visit Routes for Pet Clinics Using Particle Swarm Optimization Algorithm
DOI:
https://doi.org/10.22441/ijiem.v5i3.25971Keywords:
Home visit, Pet clinics, Travelling salesman problem, Particle sworn optimization, Google colabAbstract
High-stress levels can trigger other diseases if emotions are not channeled to reduce the feeling of stress. The stress-releasing activity that is currently trending is keeping animals, especially cats and dogs. Having a pet triggers awareness of the importance of pet care, especially in the context of home visiting services, highlighting the need for increased effectiveness of veterinary services. Determining a short route for veterinarians to visit their patients in a certain area is necessary in planning for pet clinics with home visit services. In the context of home visits by pet clinics, the Traveling Salesman Problem (TSP) is used to determine the most efficient route veterinarians can use when visiting many patients in a certain area by minimizing the total distance traveled to save time. This research uses the Particle Swarm Optimization (PSO) algorithm with 2-opt logic to solve the TSP problem. This TSP optimization was completed by utilizing Google Colab as a machine learning computing medium using Python. The results of optimizing the total distance for the best routes of home visits to pet clinics was 292.81 kilometers. This research can still be developed for more complex routes and have requirements for each route destination, such as visiting time for each patient and distance between destination points. The research is expected to impact the welfare of pets and meet community needs positively.Downloads
References
American Association of Feline Practitioners. (2016). 5 Benefts of Routine Vet Visits for Your Cat. American Association of Feline Practitioners.
Astiko, F., & Achmad Khodar. (2020). The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier. Journal of Applied Computer Science and Technology, 1(2), 61–66. https://doi.org/10.52158/jacost.v1i2.79
Azhari, M. K., Cholissodin, I., & Bachtiar, F. A. (2018). Optimasi Travelling Salesman Problem Pada Angkutan Sekolah Dengan Algoritme Particle Swarm Optimization. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(11), 5691–5699. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3403
Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform : A Comprehensive Guide for Beginners. Apress Media (1st ed.). Apress Media. https://doi.org/10.1007/978-1-4842-4470-8_29
Brigita, R. (2021). Usulan Peningkatan Kualitas Pelayanan Jasa di Pet Colony. Universitas Atma Jaya Yogyakarta.
Carneiro, T., Da Nobrega, R. V. M., Nepomuceno, T., Bian, G. Bin, De Albuquerque, V. H. C., & Filho, P. P. R. (2018). Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access, 6, 61677–61685. https://doi.org/10.1109/ACCESS.2018.2874767
Englert, M., Röglin, H., & Vöcking, B. (2014). Worst case and probabilistic analysis of the 2-opt algorithm for the TSP. Algorithmica, 68(1), 190–264. https://doi.org/10.1007/s00453-013-9801-4
G. Goldbarg, E. F., C., M., & de Souz, G. R. (2008). Particle Swarm Optimization Algorithm for the Traveling Salesman Problem. F. Greco (Ed.), Traveling Salesman Problem, 75–96. https://doi.org/10.5772/5580
Ho, J., Hussain, S., & Sparagano, O. (2021). Did the COVID-19 Pandemic Spark a Public Interest in Pet Adoption? Frontiers in Veterinary Science, 8(May), 1–5. https://doi.org/10.3389/fvets.2021.647308
Kefi, S., Rokbani, N., Krömer, P., & Alimi, A. M. (2016). A new ant supervised-PSO variant applied to traveling salesman problem. Advances in Intelligent Systems and Computing, 420, 87–101. https://doi.org/10.1007/978-3-319-27221-4_8
Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 1942–1948. https://doi.org/10.1007/978-3-031-17922-8_4
Khan, I., Maiti, M. K., & Maiti, M. (2017). Coordinating particle swarm optimization, ant colony optimization and k-opt algorithm for traveling salesman problem. Communications in Computer and Information Science, 655, 103–119. https://doi.org/10.1007/978-981-10-4642-1_10
Kurniawan, M., Farida, & Agustini, S. (2021). Rute Terpendek Algoritma Particle Swarm Optimization Dan Brute Force Untuk Optimasi Travelling Salesman Problem. Jurnal Teknik Informatika, 14(2), 191–200.
Mahi, M., Baykan, Ö. K., & Kodaz, H. (2015). A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem. Applied Soft Computing Journal, 30, 484–490. https://doi.org/10.1016/j.asoc.2015.01.068
Quinn, A. C. (2005). An examination of the relations between human attachment, pet attachment, depression, and anxiety [Ames, Iowa : Iowa State University]. https://doi.org/10.31274/rtd-180813-16451
Rakuten Insight. (2021). Pet Ownership in Asia.
Rizki, A. M., & Nurlaili, A. L. (2021). Algoritme Particle Swarm Optimization (PSO) untuk Optimasi Perencanaan Produksi Agregat Multi-Site pada Industri Tekstil Rumahan. Journal of Computer, Electronic, and Telecommunication, 1(2), 1–9. https://doi.org/10.52435/complete.v1i2.73
Sari, D. R., Feryani, A. T., & Amelia, N. (2022). How the pandemic COVID-19 influenced the consumer behavior: A case in Indonesia. Proceedings of the 5th International Research Conference on Economics and Business (IRCEB 2021), 1–9. https://doi.org/10.1201/9781003303336-1
Sekaran, U., & Bougie, R. (2016). Research Methods For Business: A Skill Building Approach (7th ed.). Wiley & Sons.
Sengkey, D. F., Kambey, F. D., Lengkong, S. P., Joshua, S. R., & Kainde, H. V. F. (2020). Pemanfaatan Platform Pemrograman Daring dalam Pembelajaran Probabilitas dan Statistika di Masa Pandemi CoVID-19. Jurnal Informatika, 15(4), 217–224.
Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S. (2022). Particle Swarm Optimization: A Comprehensive Survey. IEEE Access, 10(January), 10031–10061. https://doi.org/10.1109/ACCESS.2022.3142859
Shao, L., Bai, Y., Qiu, Y., & Du, Z. (2012). Particle Swarm Optimization Algorithm Based on Semantic Relations and Its Engineering Applications. Systems Engineering Procedia, 5, 222–227. https://doi.org/10.1016/j.sepro.2012.04.035
Soen, G. I. E., Marlina, & Renny. (2022). Implementasi Cloud Computing dengan Google Colaboratory Pada Aplikasi Pengolah Data Zoom Participants. Journal Informatic Technology And Communication, 6(1), 24–30. https://doi.org/10.36596/jitu.v6i1.781
Stohy, A., Abdelhakam, H. T., Ali, S., Elhenawy, M., Hassan, A. A., Masoud, M., Glaser, S., & Rakotonirainy, A. (2021). Hybrid pointer networks for traveling salesman problems optimization. PLoS ONE, 16(12), 1–17. https://doi.org/10.1371/journal.pone.0260995
Uddin, F., Riaz, N., Manan, A., Mahmood, I., Song, O. Y., Malik, A. J., & Abbasi, A. A. (2023). An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour. MDPI : Applied Sciences, 13(12), 1–24. https://doi.org/10.3390/app13127339
Wei, B., Xing, Y., Xia, X., & Gui, L. (2021). A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP. International Journal of Cognitive Informatics and Natural Intelligence, 15(4), 1–17. https://doi.org/10.4018/IJCINI.20211001.oa31
Yaribeygi, H., Panahi, Y., Sahraei, H., Johnston, T. P., & Sahebkar, A. (2017). The impact of stress on body function: A review. EXCLI Journal, 16, 1057–1072. https://doi.org/10.17179/excli2017-480
Zhan, Y., Wang, Z., & Wan, G. (2021). Home service routing and appointment scheduling with stochastic service times. European Journal of Operational Research, 288(1), 98–110. https://doi.org/10.1016/j.ejor.2020.05.037
Downloads
Published
How to Cite
Issue
Section
License
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above mentioned paper in whole to UMB. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors may reproduce or authorize others to reproduce the Work or derivative works for the authors personal use or for company use, provided that the source and the UMB copyright notice are indicated, the copies are not used in any way that implies UMB endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
