Penerapan Metode Particle Swarm Optimization (PSO) untuk Optimasi Waktu Tunggu pada Sistem Pemesanan Jasa Servis
DOI:
https://doi.org/10.47065/bulletincsr.v6i2.962Keywords:
Particle Swarm Optimization; Queue Optimization; Technician Scheduling; Metaheuristics; Service Information SystemAbstract
In the competitive service industry, digital transformation of booking management systems has become essential for maintaining customer loyalty. However, many enterprises still rely on manual methods that result in high latency, queue congestion, and imbalanced technician workloads. This study aims to address these inefficiencies by implementing the Particle Swarm Optimization (PSO) algorithm within a web-based service booking system architecture. PSO, a metaheuristic algorithm inspired by the social behavior of animal swarms, is employed to search for globally optimal solutions in a multidimensional search space. The algorithm is configured with 20 particles, a maximum of 100 iterations, and parameters c1 = 2.0, c2 = 2.0, and w = 0.7 to minimize cumulative customer waiting time while balancing technician task allocation based on technician availability, service duration, and operational hour constraints (08:00–16:00). Empirical testing demonstrated significant improvements in operational performance. Prior to optimization, the total customer waiting time over a three-day observation period reached 380 minutes. Following PSO implementation, waiting time was drastically reduced to 150 minutes, representing a 60.53% reduction (230 minutes saved). These findings confirm that the PSO approach not only delivers rapid and adaptive solutions to real-time data fluctuations but also enhances operational system scalability. This research provides a practical contribution for service management system developers seeking to integrate computational intelligence into the optimization of complex business processes.
Downloads
References
R. S. Chauhan, C. Thangavelu, and R. Thangarajan, “Strategic Orientation, Digital Transformation Capabilities, and Their Impact on Organizational Performance: A Comprehensive Analysis,” Journal of Information Systems Engineering and Management, vol. 10, no. 23s, pp. 530–546, Mar. 2025, doi: 10.52783/jisem.v10i23s.3752.
Y. Jia, J. Keppo, and V. Satopää, “Herding in Probabilistic Forecasts,” Manage. Sci., vol. 69, no. 5, pp. 2713–2732, May 2023, doi: 10.1287/mnsc.2022.4487.
A. A. Hammadi, “Using Digital Queues to Achieve Customer Satisfaction: The Intermediary Role of Improving Service Performance ‘An Analytical Study on the Trade Bank of Iraq-Basra Branch,’” South Asian Research Journal of Business and Management, vol. 7, no. 01, pp. 63–75, Jan. 2025, doi: 10.36346/sarjbm.2025.v07i01.006.
C. Destouet, H. Tlahig, B. Bettayeb, and B. Mazari, “Systematic review and future directions in dynamic flexible job shop scheduling: a decade of research,” J. Intell. Manuf., Oct. 2025, doi: 10.1007/s10845-025-02645-x.
S. Manna and K. S. P. Mudigonda, “Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency,” International Journal of Computers and Applications, vol. 46, no. 8, pp. 651–665, Aug. 2024, doi: 10.1080/1206212X.2024.2380660.
E. Alp, F. Pirola, R. Sala, G. Pezzotta, and B. Kuhlenkötter, “Operative service delivery planning and scheduling in Product-Service Systems,” Service Business, vol. 18, no. 2, pp. 161–192, Jun. 2024, doi: 10.1007/s11628-024-00558-y.
S. Jin, J. Tao, M. Lai, and Q. Hu, “Scheduling multi-skill technicians and reassignable tasks in a cloud computing company,” Eur. J. Oper. Res., vol. 321, no. 3, pp. 717–733, Mar. 2025, doi: 10.1016/j.ejor.2024.09.050.
T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, “Particle Swarm Optimization: A Comprehensive Survey,” IEEE Access, vol. 10, pp. 10031–10061, 2022, doi: 10.1109/ACCESS.2022.3142859.
E. H. Houssein, A. G. Gad, K. Hussain, and P. N. Suganthan, “Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application,” Swarm Evol. Comput., vol. 63, p. 100868, Jun. 2021, doi: 10.1016/j.swevo.2021.100868.
S. Surono et al., “Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO),” Emerging Science Journal, vol. 6, no. 6, pp. 1375–1393, Sep. 2022, doi: 10.28991/ESJ-2022-06-06-010.
L. Abualigah, “Particle Swarm Optimization: Advances, Applications, and Experimental Insights,” Computers, Materials & Continua, vol. 82, no. 2, pp. 1539–1592, 2025, doi: 10.32604/cmc.2025.060765.
X. Liu et al., “Integrating Attention-Enhanced LSTM and Particle Swarm Optimization for Dynamic Pricing and Replenishment Strategies in Fresh Food Supermarkets,” arXiv (Cornell University), Sep. 2025.
I. Mathlouthi, M. Gendreau, and J.-Y. Potvin, “A metaheuristic based on tabu search for solving a technician routing and scheduling problem,” Comput. Oper. Res., vol. 125, p. 105079, Jan. 2021, doi: 10.1016/j.cor.2020.105079.
M. Muhardeny, M. H. Irfani, and J. Alie, “Penjadwalan Mata Pelajaran Menggunakan Algoritma Particle Swarm Optimization (PSO) Pada SMPIT Mufidatul Ilmi,” Jurnal Software Engineering and Computational Intelligence, vol. 1, no. 1, pp. 51–63, Jun. 2023, doi: 10.36982/jseci.v1i1.3047.
A. Febiani, A. M. Widodo, N. Anwar, B. A. Sekti, and A. Yulfitri, “Implementasi Algoritma ‘Particle Swarm Optimization’ (PSO) Penjadwalan Belajar Mengajar,” IKRA-ITH Informatika?: Jurnal Komputer dan Informatika, vol. 8, no. 1, pp. 152–161, Mar. 2024, doi: 10.37817/ikraith-informatika.v8i1.3210.
D. Prasisti and Y. A. Nugroho, “Optimasi Penjadwalan Produksi untuk Meminimalkan Makespan dengan Pendekatan Particle Swarm Optimization dan Genetic Algorithm,” Jurnal Teknologi dan Manajemen Industri Terapan, vol. 2, no. 2, pp. 111–118, May 2023, doi: 10.55826/tmit.v2i2.134.
A. H. Pratama and S. Sumiati, “Optimization of Cement Bag Production Scheduling Using Particle Swarm Optimization Method,” Indonesian Journal of Innovation Studies, vol. 27, no. 1, Dec. 2025, doi: 10.21070/ijins.v27i1.1597.
J. I. R. Praveen, E. M. Malathy, Aishwarya S., Akila R., and Akshaya A., “A Hybrid PSO-ACO Algorithm to Facilitate Software Project Scheduling,” International Journal of e-Collaboration, vol. 18, no. 2, pp. 1–12, Jul. 2022, doi: 10.4018/IJeC.304039.
A. Hameed, G. I. Shahab, A. H. Rashid, and S. Mohammed, “Optimizing Queueing Systems With Metaheuristics: A Comparative Analysis Of Genetic Algorithms And Traffic Flow Inspired Optimization,” TWMS Journal of Applied and Engineering Mathematics, vol. 15, no. 8, pp. 2114–2127, 2025.
S. Dhibar, “MOHFDQ: A metaheuristic approach to optimizing hospital patient registration with a fuzzy double-orbit queueing model,” Swarm Evol. Comput., vol. 98, p. 102090, Oct. 2025, doi: 10.1016/j.swevo.2025.102090.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Metode Particle Swarm Optimization (PSO) untuk Optimasi Waktu Tunggu pada Sistem Pemesanan Jasa Servis
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Muhamad Nur, Marisa Marisa, Fadhel Rizky Pratama
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
