Optimización de disciplinas de colas con simulación de eventos discretos: Una revisión teórica y empírica

Autores/as

Palabras clave:

investigación de operaciones, análisis de costo-beneficio, modelos de programación

Resumen

El análisis de sistemas de servicio que implementan colas con disciplina de prioridad ha ganado importancia en sectores donde la eficiencia operativa es crítica. Estos modelos son fundamentales para optimizar el uso de recursos, reduciendo tiempos de espera y mejorando la respuesta del sistema frente a demandas fluctuantes. La integración de teorías de colas con prioridad y la simulación de eventos discretos facilita la evaluación de estrategias operativas en condiciones variadas, permitiendo a los administradores adaptar recursos de manera más efectiva. Investigaciones recientes han incorporado elementos de lógica difusa para abordar la incertidumbre en las tasas de llegada y servicio, proporcionando un marco más flexible y robusto para el modelado de colas. Estudios sugieren que sistemas bien gestionados no solo mejoran los indicadores de rendimiento, sino que también apoyan una toma de decisiones estratégica basada en datos. Con el avance de las tecnologías de inteligencia artificial y aprendizaje automático, se anticipa que los modelos de colas con prioridad ofrecerán aún más capacidades de predicción y adaptación, marcando un desarrollo significativo en la gestión de operaciones y sistemas informáticos. Este campo sigue siendo prolífico para la investigación, con potencial para revolucionar prácticas en industrias que dependen de la gestión eficiente de la espera y el servicio​.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Adeniran, D. A., Burodo, M. S., & Suleiman, S. (2022). Application of queuing theory and management of waiting time using multiple server model: empirical evidence from Ahmadu Bello University Teaching Hospital, Zaria, Kaduna State, Nigeria. International Journal of Scientific and Management Research, 5(4), 159-174.

Afolalu, S. A., Ikumapayi, O. M., Abdulkareem, A., Emetere, M. E., & Adejumo, O. (2021). A short review on queuing theory as a deterministic tool in sustainable telecommunication system. Materials Today: Proceedings, 44, 2884-2888.

Afolalu, S. A., Ikumapayi, O. M., Abdulkareem, A., Emetere, M. E., & Adejumo, O. (2021). A short review on queuing theory as a deterministic tool in sustainable telecommunication system. Materials Today: Proceedings, 44, 2884-2888.

Ayodeji, Y., Rjoub, H., & Özgit, H. (2023). Achieving sustainable customer loyalty in airports: The role of waiting time satisfaction and self-service technologies. Technology in Society, 72, 102106.

Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation. Prentice Hall.

Bell, S., Benatti, F., Edwards, N. R., Laney, R., Morse, D. R., Piccolo, L., & Zanetti,

O. (2018). Smart cities and M 3: Rapid research, meaningful metrics and co-design. Systemic Practice and Action Research, 31, 27-53.

Cui, S., Wang, Z., & Yang, L. (2023). A brief review of research on priority queues with self-interested customers. Innovative Priority Mechanisms in Service Operations: Theory and Applications, 1-8.

Dalmia, R., Sinha, A., Verma, R., & Gupta, P. K. (2022). Dynamic Ready Queue Based Process Priority Scheduling Algorithm. arXiv preprint arXiv:2205.07314.

Déry, J., Ruiz, A., Routhier, F., Bélanger, V., Côté, A., Ait-Kadi, D., ... & Lamontagne, M. E. (2020). A systematic review of patient prioritization tools in non-emergency healthcare services. Systematic reviews, 9, 1-14.

Dubois, D., & Prade, H. (1998). Possibility Theory in the Context of Fuzzy Logic.

Fuzzy Sets and Systems, 100, 259-276.

G. Fishman (2013). Discrete-Event Simulation: Modeling, Programming, and Analysis. Springer Science & Business Media.

Ghaleb, A., Heshmat, M., El-Sharief, M. A., & El-Sebaie, M. G. (2019, April). Using fuzzy logic and discrete event simulation to enhance production lines performance: case study. In 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 653-657). IEEE.

Ghaleb, A., Heshmat, M., El-Sharief, M. A., & El-Sebaie, M. G. (2019, April). Using fuzzy logic and discrete event simulation to enhance production lines performance: case study. In 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 653-657). IEEE.

Giambiasi, N., Escude, B., & Ghosh, S. (2000). Hybrid simulation models of discrete continuous systems. Simulation, 74(4), 191-201.

Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2008). Fundamentals of Queueing Theory. John Wiley & Sons.

Halpern, N., & Mwesiumo, D. (2021). Airport service quality and passenger satisfaction: The impact of service failure on the likelihood of promoting an airport online. Research in Transportation Business & Management, 41, 100667.

Hassan, N. A., Abdallah, N. M. S., & Attwa, R. A. (2024). Optimizing multi-skill call center staffing using queuing models: A study of service level. Journal of Applied Research and Technology, 22(2), 230-242.

Jafarnejad Ghomi, E., Rahmani, A. M., & Qader, N. N. (2019). Applying queue theory for modeling of cloud computing: A systematic review. Concurrency and Computation: Practice and Experience, 31(17), e5186.

Kataria, S., & Saini, V. (2020). The mediating impact of customer satisfaction in relation of brand equity and brand loyalty: An empirical synthesis and re- examination. South Asian Journal of Business Studies, 9(1), 62-87.

Le, T. N., Nguyen, H. M. V., Nguyen, T. A., Phung, T. T., & Phan, B. D. (2022). Optimization of load ranking and load shedding in a power system using the improved AHP algorithm. Engineering, Technology & Applied Science Research, 12(3), 8512-8519.

Li, J., & Li, Q. (2023). Analysis of queue management in theme parks introducing the fast pass system. Heliyon, 9(7).

Liu, P., Jiang, T., & Chai, X. (2020). Performance analysis of queueing systems with a particular service interruption discipline. Discrete Dynamics in Nature and Society, 2020, 1-12.

Lu, Y., Musalem, A., Olivares, M., & Schilkrut, A. (2013). Measuring the effect of queues on customer purchases. Management Science, 59(8), 1743-1763.

Ma, W. M., Zhang, H., & Wang, N. L. (2019). Improving outpatient satisfaction by extending expected waiting time. BMC Health Services Research, 19, 1-7.

Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modeling and simulation. Journal of Simulation, 4(3), 151-162.

Nosek Jr, R. A., & Wilson, J. P. (2001). Queuing theory and customer satisfaction: a Review of terminology, trends, and applications to pharmacy practice. Hospital pharmacy, 36(3), 275-279.

Olayinka, W. (2024, enero 16). A comprehensive guide to queue management systems in banks. WaitWell Queue Management Software. https://waitwell.ca/queue-management-systems-in-banks/

Raicu, S., Costoscu, D., & Popa, M. (2023). Effects of the Queue Discipline on System Performance. AppliedMath, 3(1), 37-48.

Roy, D., Spiliotopoulou, E., & de Vries, J. (2022). Restaurant analytics: Emerging practice and research opportunities. Production and Operations Management, 31(10), 3687-3709.

Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo Method.

John Wiley & Sons.

Sadeghi, N., Fayek, A. R., & Seresht, N. G. (2015). Queue performance measures in construction simulation models containing subjective uncertainty. Automation in Construction, 60, 1-11.

Serper, N., Elif, Ş. E. N., & ÇALIŞ, B. (2022). Discrete event simulation model performed with data analytics for a call center optimization. Istanbul Business Research, 51(1), 189-208.

Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw Hill.

Tan, K. W., Lau, H. C., & Lee, F. C. Y. (2013, December). Improving patient length- of-stay in emergency department through dynamic queue management. In 2013 Winter Simulations Conference (WSC) (pp. 2362-2373). IEEE.

Tian, Y., Zhao, M., Liu, M., Liao, Y., Huang, C., & Hu, M. (2022). Hybrid modeling methodology for integrating customers’ behaviors into system simulation to improve service operations management. Simulation Modelling Practice and Theory, 115, 102445.

Ritha, W., & Robert, L. (2010). Fuzzy queues with priority discipline. Applied Mathematical Sciences, 4(12), 575-582.

Ferrand, Y. B., Magazine, M. J., Rao, U. S., & Glass, T. F. (2018). Managing responsiveness in the emergency department: Comparing dynamic priority queue with fast track. Journal of Operations Management, 58, 15-26.

Khot, A. S., & Mishra, R. K. (2017). Learning Functional Data Structures and Algorithms. Packt Publishing Ltd.

Gioia, D. G., & Minner, S. (2023). On the value of multi-echelon inventory management strategies for perishable items with on-/off-line channels. Transportation Research Part E: Logistics and Transportation Review, 180, 103354.

Cueto, L. J., Frisnedi, A. F. D., Collera, R. B., Batac, K. I. T., & Agaton, C. B. (2022). Digital innovations in MSMEs during economic disruptions: experiences and challenges of young entrepreneurs. Administrative Sciences, 12(1), 8.

Descargas

Publicado

2025-03-09

Cómo citar

Morones Ruelas, D., Rodríguez Pérez, R. E., & Castro Lugo, D. (2025). Optimización de disciplinas de colas con simulación de eventos discretos: Una revisión teórica y empírica. Equilibrio Económico. Revista De Economía, Política Y Sociedad, 21(59), 58-81. https://revistas.uadec.mx/index.php/equilibrioeconomico/article/view/73

Artículos similares

1-10 de 29

También puede Iniciar una búsqueda de similitud avanzada para este artículo.