Improving Veterinary Service Efficiency: Optimizing Home Visit Routes for Pet Clinics Using Particle Swarm Optimization Algorithm

Erico Sofyan Chrissandhi; Christa Dian Pratiwi; Pringgo Widyo Laksono

doi:10.22441/ijiem.v5i3.25971

Authors

Erico Sofyan Chrissandhi Department of Industrial Engineering, Master’s Degree Program, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Christa Dian Pratiwi Department of Industrial Engineering, Master’s Degree Program, Universitas Sebelas Maret, Surakarta 57126, Indonesia
Pringgo Widyo Laksono Department of Industrial Engineering, Master’s Degree Program, Universitas Sebelas Maret, Surakarta 57126, Indonesia

DOI:

https://doi.org/10.22441/ijiem.v5i3.25971

Keywords:

Home visit, Pet clinics, Travelling salesman problem, Particle sworn optimization, Google colab

Abstract

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.

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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