Simulation modelling of a queuing system to improve customer satisfaction
DOI:
https://doi.org/10.55573/JISED.118761Keywords:
Discrete Event Simulation, Queuing System, Arena SimulationAbstract
This study aims to develop a Discrete Event Simulation (DES) model to represent and evaluate the existing service process at JPJ UTC Kota Bharu, Kelantan. The study focuses on the queuing system experienced by customers during the service process, where waiting times and queue formation may affect overall service efficiency. Arena simulation software was employed to model the existing system and evaluate its performance using key performance measures, including average waiting time, total time spent in the system, resource utilization, and queue length. The simulation model was further used to identify bottlenecks and assess the performance of alternative improvement scenarios. The results provide a comprehensive evaluation of the existing service system and demonstrate the potential effects of the proposed alternatives on queuing performance. The findings indicate that the selected alternative can improve the efficiency of the service process by reducing customer waiting time and overall time spent in the system. The proposed simulation approach provides useful insights for supporting operational decision-making and improving the efficiency and quality of customer services at JPJ UTC Kota Bharu.
Downloads
References
Brady, S. S. et. al. (2020). Development of conceptual models to guide public health research, practice, and policy: synthesizing traditional and contemporary paradigms. Health Promotion Practice, 21(4), 510–524. https://doi.org/10.1177/1524839919890869
C. G. Panayiotou, C. G. Cassandras and Wei-Bo Gong, "Model abstraction for discrete event systems using neural networks and sensitivity information," in 2000 Winter Simulation Conference Proceedings (Cat.No.00CH37165), Orlando, FL, USA, 2000, vol.1, pp. 335-341, https://doi.org/10.1109/WSC.2000.899736
D.-U. Kim, M.-S. Jie, and W.-H. Choi, “Airport Simulation Based on Queuing Model Using ARENA,” International Journal of Advanced Science and Technology, vol. 115, pp. 125–134, 2018.
F. H. A. Rahim, N. N. Hawari, and N. Z. Abidin. “Supply & Demand of Rice in Malaysia: A System Dynamics Approach,” International Journal of Supply Chain Management, vol. 6, no. 4, pp.1-7, December 2017.
H. Sapiri, J. Zulkepli, N. Ahmad, N. Z. Abidin, and N. N. Hawari, Introduction to System Dynamics Modelling And Vensim Software. Sintok, Kedah. UUM Press, 2017.
H. He and Z. Hu, “Analysis of Fast Food Service Capability Based on Flexsim Modeling and Simulation,” IOP Conference Series: Materials Science and Engineering, vol. 394, p. 052005, 2018, doi: https://doi.org/10.1088/1757-899X/394/5/052005
H. Eskandari, M. Riyahifard, S. Khosravi, and C. D. Geiger, “Improving the emergency department performance using simulation and MCDM methods,” in Proceedings of the 2011 Winter Simulation Conference (WSC), 2011, pp. 1211-1222.
J. Lim, N. Azriani and M. Nor, “Simulation by Queuing System at Immigration Department,” International Academic Research Journal of Social Science, vol. 2, no. 1, pp. 112-119, 2016.
M. H. Alhaag, T. Aziz and I. M. Alharkan, "A queuing model for health care pharmacy using software Arena," in 2015 International Conference on Industrial Engineering and Operations Management (IEOM), Dubai, United Arab Emirates, 2015, pp. 1-11, doi: 10.1109/IEOM.2015.7093849
M. Z. H. A. Jalal, W. L. H. M. Desa, M. K. M. Nawawi, R. Khalid, R. Ramli, W. K. Abduljabbar, “Road Traffic Congestion Solution sing Discrete Event Simulation,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 5S, pp. 53-57, 2018.
N. Madadi, A. H. Roudsari, K. Y. Wong, and M. R. Galankashi, “Modeling and Simulation of a Bank Queuing System,” in Proceedings - 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 209-215.
P. Özkan, S. Süer, İ. K. Keser, and İ. D. Kocakoç, “The effect of service quality and customer satisfaction on customer loyalty: The mediation of perceived value of services, corporate image, and corporate reputation,” International Journal of Bank Marketing, vol. 38, no. 2, pp. 384–405, 2019, doi: https://doi.org/10.1108/IJBM-03-2019-0096
R. M. Tahar, A practical approach to computer simulation modelling. Serdang, Selangor: Universiti Putra Malaysia Press, 2006.
Swaen, B. & George, T. (2024, March 18). What Is a Conceptual Framework? | Tips & Examples. Scribbr. https://www.scribbr.com/methodology/conceptual-framework/
S. Gumus, G. M. Bubou, and M. H. Oladeinde, “Application of queuing theory to a fast food outfit: a study of blue meadows restaurant,” Independent Journal of Management & Production, vol. 8, no. 2, pp. 441-458, 2017, doi: https://doi.org/10.14807/ijmp.v8i2.576
S. Madankumar and C. Rajendran, “A mixed integer linear programming model for the vehicle routing problem with simultaneous delivery and pickup by heterogeneous vehicles, and constrained by time windows,” Sādhanā, vol. 44, no. 39, 2019, doi: https://doi.org/10.1007/s12046-018-1048-y
S. Sharifi, S. M. Sajadi, and M. M. Tavakoli, “Simulation process of Isfahan post office using Arena,” International Journal of Services and Operations Management, vol. 17, no. 1, pp. 50-66, 2014, doi: 10.1504/IJSOM.2014.057995
T. C. Xian, C. W. Hong, and N. N. Hawari, “Modeling and simulation of queuing system for customer service improvement: A case study”, in The 4th International Conference on Quantitative Sciences and its Applications (ICOQSIA 2016), 2016, vol. 1782, no. 1. doi:10.1063/1.4966087
White, K. P., & Ingalls, R. G. (2009). Introduction to simulation. Proceedings - Winter SimulationConference, 9(1),12–23. https://doi.org/10.1109/WSC.2009.5429315
