Determining Optimal Service Facilities Using a Simulation Approach at Restaurant
DOI:
https://doi.org/10.29103/sisfo.v10i2.27723Keywords:
Queue, Simulation, Arena, Waiting Time, Queuing CostAbstract
The rapid growth of the fast-food industry has intensified competition, requiring continuous service quality improvements to maintain customer satisfaction and loyalty. At a popular restaurant, customers often face long queues, with waiting times ranging from 8 to 35 minutes, potentially reducing satisfaction and repeat visits. This study examines the operational conditions of a restaurant that implements a single-channel service system with two process stages: cashier service and food preparation and serving in the kitchen. Observations show that total service time ranges from 8 to 35 minutes, consisting of cashier time of approximately 2–15 minutes and kitchen processing time of 6–20 minutes, with significant variation leading to the formation of long queues. This situation indicates a major bottleneck in the system, particularly in the kitchen during surges in demand, which results in customers leaving the queue before being served and potentially leading to lost revenue. Therefore, this study applies a simulation approach to represent the real conditions of restaurant operations, identify bottlenecks, and evaluate alternative service facility improvements in a structured manner without disrupting operational activities, in order to obtain a more optimal system configuration in terms of waiting time, service capacity, and cost efficiency. This study aims to identify the most suitable queuing model and optimal service facility configuration to reduce both waiting time and queuing costs. Data were collected over three consecutive days through direct observation (1:00 PM–9:00 PM WIB), covering customer arrival, cashier service, food preparation, and order pickup times. Analysis was performed using Arena software supported by distribution testing and scenario simulations. The best improvement scenario involved adding one kitchen station staffed by two employees, reducing the average waiting time from 21 to 13 minutes and achieving a total queuing cost of Rp 227,237.4 per hour. Clarity in cost calculations is necessary so that scenario selection truly reflects the consideration between increasing service capacity and costs that must be borne. However, the lack of explanation regarding the method for assessing customer waiting time costs, assumed labor wage rates, and the basis for calculating operational costs makes the results of the economic analysis difficult to retest and verify. Thus, although the simulation results indicate a decrease in waiting time, the validity of decisions in optimizing service facilities still needs to be supported by a more detailed, measurable, and systematic description of the cost structure. This adjustment resulted in a multi-channel, multi-phase system, enhancing operational efficiency and customer satisfaction. The urgency of this research is important because restaurants experience an imbalance between service capacity and high demand during peak hours. This condition causes long queues and increased customer waiting times. This has an impact on decreased satisfaction and the potential loss of customers who leave the queue. A simulation approach is needed to test various scenarios and determine the most optimal service configuration efficiently.
References
[1] G. B. HM, M. A. Syahry, R. I. Rachmawan, L. A. Putri, M. F. Mustanir, and M. D. Amrullah, “Analisis Sistem Antrian dengan Metode Simulasi Menggunakan Software Arena 14 Pada Wizzmie Tunjungan,” in Prosiding Seminar Nasional Teknologi Industri Berkelanjutan IV (SENASTITAN IV), Institut Teknologi Adhi Tama Surabaya (ITATS), 2024, pp. 1–6. [Online]. Available: https://ejurnal.itats.ac.id/senastitan/article/view/5602
[2] A. Fikriansyah, T. . Reza, and H. Shafariah, “Pengaruh Kualitas Pelayanan dan Lokasi Terhadap Kepuasan Pelanggan Foodpedia Cempaka Putih Jakarta Pusat,” JAMBIS J. Adm. Bisnis, vol. 2, no. 1, pp. 1–9, 2022, doi: 10.31334/jambis.v2i1.2451.
[3] M. Nur and F. F. Qitri, “Analisa Sistem Antrian Loket pada PT. Tiki Jalan Teuku Umar Pekanbaru dengan Menggunakan Software Arena,” J. Tek. Ind. J. Has. Penelit. dan Karya Ilm. dalam Bid. Tek. Ind., vol. 2, no. 2, pp. 212–219, 2016, doi: 10.24014/jti.v2i2.5104.
[4] M. Y. Ramadhan and D. Herwanto, “Analisis Ukuran Kinerja Sistem Pelayanan Pada Antrian Alfamidi Jalan Hs. Ronggo Waluyo Karawang,” Nusant. Eng., vol. 4, no. 1, pp. 35–44, 2021, doi: https://ojs.unpkediri.ac.id/index.php/noe/article/view/15909/2013.
[5] A. Pitriyani and A. Ilmaniati, “Analisis Parameter Antrean Loket Pendaftaran Poliklinik UPTD Puskesmas Rawat Inap Ciranjang dengan Metode Simulasi Menggunakan Arena 14.0,” in Prosiding Seminar Nasional Teknologi Industri Berkelanjutan IV (SENASTITAN IV), Institut Teknologi Adhi Tama Surabaya (ITATS), 2024, pp. 1–7. [Online]. Available: https://ejurnal.itats.ac.id/senastitan/article/view/5702
[6] H. B. S. Langgeng, H. Nuha, and H. Murnawan, “Analisis Sistem Antrian Pelayanan Bongkar Muat Kapal Tongkang Batu Bara pada Mother Vessel untuk Meminimalisir Waktu Bongkar Muat pada PT. Handil Bhakti Persada,” J. Tek. Ind., vol. 12, no. 2, pp. 133–143, 2022, doi: 10.25105/jti.v12i2.15638.
[7] S. Dewanto, A. Santosa, and D. Andriani, “Simulasi Sistem Pelayanan Rawat Jalan di Rumah Sakit Menggunakan Simulasi Kejadian Diskrit,” Ina. J. Ind. Qual. Eng., vol. 8, no. 1, pp. 25–36, 2020, doi: 10.34010/iqe.v8i1.2725.
[8] B. H. Purnomo, B. Suryadharma, and N. Y. Ekasari, “Model Sistem Antrian Pada Pelayanan Restoran Cepat Saji (Studi Kasus di KFC Gajah Mada Kabupaten Jember),” J. Agroteknologi, vol. 15, no. 1, pp. 40–58, 2021, doi: 10.19184/j-agt.v15i01.19929.
[9] M. Haviv; and L. Ravner, “A Survey of Queueing Systems with Strategic Timing of Arrivals,” arXiv, 2020, doi: 10.48550/arXiv.2006.12053.
[10] X. Chen, Y. Liu, and G. Hong, “An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems,” arXiv, 2020, doi: 10.48550/arXiv.2009.02911.
[11] W. Hwang, Y. Kim, and K. Lee, “A Transient Queueing Analysis under Time-varying Arrival and Service Rates for Enabling Low-Latency Services,” arXiv, 2017, doi: 10.48550/arXiv.1704.07091.
[12] A. Daw, B. Fralix, and J. Pender, “Non-stationary Queues with Batch Arrivals,” arXiv, 2020, doi: 10.48550/arXiv.2008.00625.
[13] M. Haviv and L. Ravner, “A Survey of Queueing Systems with Strategic Timing of Arrivals,” arXiv, 2020, doi: 10.48550/arXiv.2006.12053.
[14] Y. R. Pasaribu, M. V. Maulana, S. R. Safiri, S. Qalbi, and D. N. Artaviana, “Modeling Analysis and Queuing System at Mie Gacoan Restaurant,” J. Multidisiplin Indones., vol. 4, no. 6, pp. 371–383, 2025, doi: 10.58344/jmi.v4i6.2328.
[15] N. U. I. Hossain, M. Lutfi, I. Ahmed, and H. Debusk, “Application of systems modeling language (SysML) and discrete event simulation to address patient waiting time issues in healthcare,” Smart Heal., vol. 29, p. 100403, 2023, doi: 10.1016/j.smhl.2023.100403.
[16] L. J. Hong, Y. Song, and T. Wang, “Fast Discrete-Event Simulation of Markovian Queueing Networks through Euler Approximation,” arXiv, 2024, doi: 10.48550/arXiv.2402.13259.
[17] D. A. Putera, A. A. Dermawan, A. Lawi, P. T. Saputra, A. N. K. Pane, and S. N. Maulidina, “Analysis of the Queuing System in XYZ Food Industry in Batam City (Case Study of Queuing Time Observation),” J. Sist. Tek. Ind., vol. 26, no. 2, pp. 242–257, 2024, doi: 10.32734/jsti.v26i2.16688.
[18] D. Mei, R. Ristanti, F. P. Anasfi, and E. Sumariyono, “Simulation of The Queue System in Restaurant Mie Gacoan Branch Gresk Using Arena 14.0,” Tibuana J. Appl. Ind. Eng., vol. 7, no. 1, pp. 23–34, 2024, doi: 10.36456/tibuana.7.01.7938.23-34.
[19] E. Che, J. Dong, and H. Namkoong, “Differentiable Discrete Event Simulation for Queuing Network Control,” arXiv, 2024, doi: 10.48550/arXiv.2409.03740.
[20] A. F. B. Mazri, J. K. Cheng, and Y. Liu, “Application of Discrete Event Simulation for Enhancing Queuing System at Lotus Alor Setar,” Int. J. Ind. Manag., vol. 19, no. 4, pp. 197–209, 2025, doi: 10.15282/ijim.19.4.2025.11959.
[21] F. O. Usluer, “Solving Queue Problems in a Campus Dining Service with Discrete Event Simulation,” Ege Acad. Rev., vol. 25, no. 2, pp. 351–360, 2025, doi: 10.21121/eab.20250207.
[22] K. Gulhane, “Enhancing Queuing Efficiency Using Discrete Event Simulation,” Int. J. Logist. Syst. Manag., vol. 36, no. 4, pp. 531–546, 2020, doi: 10.1504/IJLSM.2020.108930.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Riva Bahari, Cut Ita Erliana, Fatimah Fatimah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors retain copyright and grant the journal right of first publication and this work is licensed under a Creative Commons Attribution-ShareAlike 4.0 that allows others to share the work with an acknowledgement of the works authorship and initial publication in this journal.
All articles in this journal may be disseminated by listing valid sources and the title of the article should not be omitted. The content of the article is liable to the author.
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.
In the dissemination of articles by the author must declare the Sisfo: Jurnal Ilmiah Sistem Informasi as the first party to publish the article.
