Optimization of Nursing Scheduling in Emergency by Using Genetic Algorithm
DOI:
10.29303/jpm.v19i1.6412Published:
2024-01-20Issue:
Vol. 19 No. 1 (2024): January 2024Keywords:
Attendance; Genetic Algorithms; Nurse; Optimization; SchedulingArticles
Downloads
How to Cite
Downloads
Metrics
Abstract
Scheduling nurse duty is one of the problems in health organizations that is quite complicated to solve. Starting from the uncertain number of patients, serious patient illnesses, characteristics of organizational groups, requests for nurses to take time off, and the qualifications and specialization of the nurses themselves are why scheduling in the ER is difficult to optimize. The same thing is being experienced by one of the health institutions, RSUD Dr. Pirngadi. Preparing schedules or determining the number of nurses on duty is still done manually, resulting in a lack of optimization in scheduling and the number of nurses who must be on duty, especially in the emergency department. In solving this problem, an appropriate method is needed so that the process of scheduling and optimizing the number of nurses can be formed properly. This research applies the Genetic Algorithm in optimal emergency department (IGD) nurse duty scheduling. Genetic algorithms, also called search algorithms, are based on the mechanisms of natural selection and genetics. Genetic algorithms are one of the appropriate methods for solving complex optimization problems. This method is good enough to optimize shift scheduling for the Emergency Room Nursing Service in a Hospital. This Genetic Algorithm can be a solution to multi-criteria and multi-objective problems modeled using biological and evolutionary processes. So, the concept of this method can be applied in optimizing the Nursing Service schedule. The results of calculations using the Genetic Algorithm show quite significant comparisons, including several nurses losing their positions and being eliminated by mutation because they could not compete with several other strong individuals.
References
Sam'ani, M., Surarso, B., & Eko Adi Sarwoko, M. (2012). Rancang Bangun Sistem Penjadwalan Perkuliahan dan Ujian Akhir Semester Dengan Pendekatan Algoritma Genetika (Doctoral dissertation, Universitas Diponegoro).
Gen, M., Li, Y., & IDA, K. (2000). Spanning tree-based genetic algorithm for bicriteria fixed charge transportation problem. Journal of Japan Society for Fuzzy Theory and Systems, 12(2), 295-303.
Asmoro, M. P., & Siregar, T. (2022). Terapi Self Healing Menggunakan Metode Expressive Writing Therapy untuk Mengatasi Stres Kerja Perawat. Pradina Pustaka.
Seniwati, S., Anugrahwati, R., Silitonga, J. M., Hutagaol, R., Gunawan, D., Sihura, S. S. G., ... & Solehudin, S. (2022). Buku Manajemen Keperawatan.
Suyanto (2005). Algoritma Genetika dalam MATLAB, Andi,Yogyakarta.
Puspita, R. M., Arini, A., & Masrurah, S. U. (2016). Pengembangan aplikasi penjadwalan kegiatan pelatihan teknologi informasi dan komunikasi dengan algoritma genetika (studi kasus: bprtik). Jurnal Online Informatika, 1(2), 76-81.
Yuliandar, D., Warsito, B., & Yasin, H. (2012). Pelatihan feed forward neural network menggunakan algoritma genetika dengan metode seleksi turnamen untuk data time series. Jurnal Gaussian, 1(1), 65-72.
Joni, I. D. M. A. B., & Nurcahyawati, V. (2012). Penentuan Jarak Terpendek Pada Jalur Distribusi Barang Di Pulau Jawa Dengan Menggunakan Algoritma Genetika. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 1(3), 244-258.
Sugeha, I. H., Inkiriwang, R. L., & Pratasis, P. A. (2019). Optimasi penjadwalan menggunakan metode algoritma genetika pada proyek rehabilitasi puskesmas minanga. Jurnal Sipil Statik, 7(12).
Hutagalung, D. M. (2018). Konsep Algoritma Genetika Sebagai Dasar Pembuatan Teka Teki Silang Berbasis Komputer. Jurnal Mahajana Informasi, 3(1), 1-7.
Ismayanto, E., Djamal, E. C., & Komarudin, A. (2015). Optimalisasi Penempatan Barang Dalam Ruang 3 (Tiga) Dimensi Menggunakan Algoritma Genetika.
Saputra, G., & Hamsi, A. (2014). Analisa Optimasi Pemesinan pada Mesin Bor Breda Tipe R-35 dengan Algoritma Genetika. J. e-Dinamis, 9(1), 184-193.
Pratama, Y. (2021). Optimalisasi Penjadwalan Karyawan Paruh Waktu Berdasarkan Nilai Fitness Terbaik Menggunakan Algoritma Genetika. Jurnal Nasional Informatika (Junif), 2(2), 114-142.
Maharani, F. (2013). Sistem Penjadwalan Proyek Menggunakan Algoritma Genetika (Doctoral Dissertation, Universitas Islam Negeri Sultan Syarief Kasim Riau).
} Adriana, F., & Fadly, R. L. (2015). Algoritma Genetika dan Penerapannya.
Susanto, S., Rachmat, R., & Hardiantono, D. (2018). Rancang Bangun Sistem Penjadwalan Kuliah Jurusan Teknik Informatika Fakultas Teknik Universitas Musamus Merauke Menggunakan Algoritma Genetika. Musamus Journal of Technology & Information, 1(01), 33-41.
Grefenstette, John J. "Optimization of control parameters for genetic algorithms." IEEE Transactions on systems, man, and cybernetics 16.1 (1986): 122-128.
A.T Ernst, H Jiang, M Krishnamoorthy. (2004). Staff scheduling and rostering: A review of applications, methods, and models. European Journal of Operational Research, Volume 153, Issue 1, Pages 3-27.
Cipta, H. (2023). Graph Coloring Implementation Using Welch Powell Algorithm In Lecture Scheduling Design For Mathmatics Department. Mathline: Jurnal Matematika dan Pendidikan Matematika, 8(4), 1383-1398.
Cipta, H., & Widyasari, R. (2020). The Determination of Shortest Path Using Genetics Algorithm Assisted Matlab. IJISTECH (International Journal of Information System and Technology), 3(2), 302-308.
Haupt, R. L., & Haupt, S. E. (2004). Practical Genetic Algorithms. New Jersey.: John Wiley & Sons, Inc.
Author Biographies
Mhd Panerangan Hasibuan, Department of Matematics, Fakulty of Science and Technlogy, Universitas Islam Negeri Sumatera Utara, Medan
Hendra Cipta, Department of Mathmatics, Fakulty of Science and Technology, Universitas Islam Negeri Sumatera Utara
License
Copyright (c) 2024 Mhd Panerangan Hasibuan, Hendra Cipta
This work is licensed under a Creative Commons Attribution 4.0 International License.
The following terms apply to authors who publish in this journal:
1. Authors retain copyright and grant the journal first publication rights, with the work simultaneously licensed under a Creative Commons Attribution License 4.0 International License (CC-BY License) that allows others to share the work with an acknowledgment of the work's authorship and first publication in this journal.
2. Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., posting it to an institutional repository or publishing it in a book), acknowledging its initial publication in this journal.
3. Before and during the submission process, authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website), as this can lead to productive exchanges as well as earlier and greater citation of published work (See The Effect of Open Access).