Solving Queue Problems in a Campus Dining Service with Discrete Event Simulation

Abstract

Waiting in long queues can be experienced in various ways. This issue arises due to insufficient capacity to instantly satisfy demand. An increase in waiting time leads to the formation of queues. Campus dining service (CDS) facilities are susceptible to experiencing queues when their capacity is insufficient. While queues may be a natural occurrence, customer satisfaction is greatly affected when the waiting time exceeds a certain threshold. In order to maximize customer satisfaction and ensure timely service delivery, measures should be implemented to manage queues. Although addressing queue problems in a system is an admirable goal, it is a complex task to identify the source of a queue and develop cost-effective solutions. In this research, a discrete event simulation model of a CDS was developed and analyzed. The study aims to devise low-cost solutions for queue problems through scenario-based analysis. Each scenario is based on a hypothesis, considering existing literature and the systems subjected to improvements, which form the basis for these hypotheses. The results indicate that capacity reallocation solutions are the most effective in resolving long queue problems. The findings, evaluations, and recommendations are presented in the results section.

Keywords

• Discrete Event Simulation
• Campus Dining Service
• Operations Research
• Queue Management
• Capacity Reallocation

References

1. Aslan, E., & Özderiir, N. (2021). Bir Üniversite Öğrenci Yemekhanesinde Simülasyon Uygulaması. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 18(2), 965–978. https://doi.org/10.33437/ksusbd.629888
2. Curin, S. A., Vosko, J. S., Chan, E. W., & Tsimhoni, O. (2005). Reducing Service Time at a Busy Fast Food Restaurant on Campus. Proceedings of the Winter Simulation Conference, 2005., 2628–2635. https://doi.org/10.1109/WSC.2005.1574562
3. Firmansyah, F., & Saputra, A. C. (2021). Effect Of The Implementation Of Queue System On Customer Satisfaction. Bongaya Journal for Research in Management (BJRM), 4(1), 1–7. https://doi.org/10.37888/bjrm.v4i1.245
4. Ghaleb, M. A., Suryahatmaja, U. S., & Alharkan, I. M. (2015). Modeling and simulation of Queuing Systems using arena software: A case study. 2015 International Conference on Industrial Engineering and Operations Management (IEOM), 1–7. https://doi.org/10.1109/IEOM.2015.7093945
5. Habibi, N. (2017). Higher education policies and overeducation in Turkey. European Journal of Higher Education, 7(4), 440–449. https://doi.org/10.1080/21568235.2017.1308832
6. Harrison, H., King, H., Mchudleigh, Sawong, Gsavinkoff, JenchuiAusenco, & Pierre-Luc Paour. (2016). Jaamsim: Release 2023-02 (Version v2023-02) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.57118
7. He, H., & Hu, Z. (2018). Analysis of Fast Food Service Capability Based on Flexsim Modeling and Simulation. IOP Conference Series: Materials Science and Engineering, 394, 052005. https://doi.org/10.1088/1757-899X/394/5/052005
8. Ivan, J. (2021). The Impact of the Constraints of Class Scheduling on Campus Dining: A Simulation-based Case Study. Proceedings of the 33rd European Modeling & Simulation Symposium, 266–271. https://doi.org/10.46354/i3m.2021.emss.037

9. Kambli, A., Sinha, A. A., & Srinivas, S. (2020). Improving campus dining operations using capacity and queue management: A simulation-based case study. Journal of Hospitality and Tourism Management, 43, 62–70. https://doi.org/10.1016/j.jhtm.2020.02.008
10. King, D. H., & Harrison, H. S. (2013). Open-source simulation software “JaamSim”; 2013 Winter Simulations Conference (WSC), 2163–2171. https://doi.org/10.1109/WSC.2013.6721593
11. Lang, S., Reggelin, T., Müller, M., & Nahhas, A. (2021). Open-source discrete-event simulation software for applications in production and logistics: An alternative to commercial tools? Procedia Computer Science, 180, 978–987. https://doi.org/10.1016/j.procs.2021.01.349
12. Lillo, F., Gonzalez, A., Severino-Gonzalez, P., Toro-Lagos, V., & Rojas-Sepulveda, I. (2022). Capacity in University Food Services: A Simulation Analysis Based on the case of Universidad Católica del Maule. Review of Economics and Finance, 20, 805–815. https://doi.org/10.55365/1923.x2022.20.91
13. Nettles, M. F., & Gregoire, M. B. (1997). Use Of Computer Simulation In School Foodservice. Foodservice Research International, 9(3), 143–156. https://doi.org/10.1111/j.1745-4506.1997.tb00316.x
14. Ning, J., Li, J., Li, Z., Tian, Y., & Li, S. (2023). Study on The Form of Reservation of Food Orders in Campus Canteens in The Post-epidemic Era. International Journal of Education and Humanities, 8(2), 96–102. https://doi.org/10.54097/ijeh.v8i2.7754
15. Osuna, E. E. (1985). The Psychological Cost of Waiting. Journal of Mathematical Psychology, 29(1), 82–105. https://doi.org/10.1016/0022-2496(85)90020-3
16. Pidd, M. (2004). Computer Simulation in Management Science (5. ed). Wiley.
17. Raczynski, S. (2022). Discrete Event Models. In S. Raczynski, Models for Research and Understanding (pp. 171-187). Springer International Publishing. https://doi.org/10.1007/978-3-031-11926-2_7
18. Sargent, R. G., & Balci, O. (2017). History of verification and validation of simulation models. 2017 Winter Simulation Conference (WSC), 292–307. https://doi.org/10.1109/WSC.2017.8247794
19. Smith, R. A., White-McNeil, A., & Ali, F. (2020). Students’ perceptions and behavior toward on-campus foodservice operations. International Hospitality Review, 34(1), 13–28. https://doi.org/10.1108/IHR-06-2019-0010
20. Taha, H. A. (2018). Operations Research: An Introduction (10. ed). Pearson India.
21. Wooten, R., Lambert, L. G., & Joung, H.-W. (2018). Evaluation of students’ satisfaction with three all-you-can-eat university dining facilities. Journal of Foodservice Business Research, 21(5), 539–552. https://doi.org/10.1080/15378020.2018.1483691