1. | Aksin, Z., Armony, M., & Mehrotra, V. (2007). The modern call center: A multi-disciplinary perspective on operations management research. Production and Operations Management, 16: 665-688. doi:10.1111/j.1937-5956.2007.tb00288.x. |
2. | Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., Zhao, L. (2005). Statistical analysis of a telephone call center. Journal of the American Statistical Association 100(469): 36–50 |
3. | Bylina, J., Bylina, B., Zoła, A., Skaraczynski, T. (2009). A Markovian model of a call center with time varying arrival rate and skill based routing. Computer Networks 2009. Springer Science Business Media: 26–33. |
4. | Burak, M. (2014). Multi-step uniformization with steady-state detection in nonstationary m/m/s queuing systems. arXiv preprint arXiv:1410.0804 |
5. | Burak, M.(2015). Inhomogeneous CTMC Model of a Call Center with Balking and Abandonment. Studia Informatica Vol.36 No.2(120): 23-34. |
6. | Czachórski, T., Fourneau, J.M., Nycz, T., Pekergin, F. (2009). Diffusion approximation model of multiserver stations with losses. Electronic Notes in Theoretical Computer Science 232: 125–143. |
7. | Czachórski, T., Nycz, T., Nycz, M., Pekergin, F. (2014). Traffic engineering: Erlang and engset models revisited with diffusion approximation. Information Sciences and Systems 2014. Springer Science – Business Media: 249–256. |
8. | Deslauriers, A., L’Ecuyer, P., Pichitlamken, J., Ingolfsson, A., Avramidis, A.N. (2007). Markov chain models of a telephone call center with call blending. Computers &Operations Research 34(6): 1616–1645 |
9. | Gans, N., Koole, G., Mandelbaum, (2003). A.: Telephone call centers: Tutorial, review, and research prospects. Manufacturing & Service Operations Management 5(2): 79–141 |
10. | Green, L. V., Kolesar, P. J., & Whitt, W. (2007). Coping with time-varying demand when setting staffing requirements for a service system. Production and Operations Management, 16: 13-39. doi:10.1111/j.1937-5956.2007.tb00164.x. |
11. | Green, L. V., & Soares, J. (2007). Computing time-dependent waiting time probabilities in m(t)/m/s(t) queuing systems. Manufacturing & Service Operations Management, 9: 54-61. doi:10.1287/msom.1060.0127. |
12. | Gross, D., & Miller, D. R. (1984). The randomization technique as a modeling tool and solution procedure for transient Markov processes. Operations Research, 32: 343-361. doi:10.1287/opre.32.2.343. |
13. | Haverkort, B. R. (2001). Markovian models for performance and dependability evaluation. Lecture Notes in Computer Science. Springer Science - Business Media:38-83. doi:10.1007/3-540-44667-2_2. |
14. | Ingolfsson, A., Akhmetshina, E., Budge, S., Li, Y., & Wu, X. (2007). A survey and experimental comparison of service-level-approximation methods for nonstationary m(t)/m/s(t) queueing systems with exhaustive discipline. INFORMS Journal on Computing, 19: 2 |
15. | Ingolfsson, A., Campello, F., Wu, X., & Cabral, E. (2010). Combining integer programming and the randomization method to schedule employees. European Journal of Operational Research, 202: 153-163. doi:10.1016/j.ejor.2009.04.026. |
16. | Malhotra, M., Muppala, J. K., & Trivedi, K. S. (1994). Stiffness-tolerant methods for transient analysis of stiff Markov chains. Microelectronics Reliability, 34: 1825-1841. doi:10.1016/0026-2714(94)90137-6. |
17. | Phung-Duc, T., Kawanishi, K. (2014). Performance analysis of call centers with abandonment, retrial and after-call work. Performance Evaluation 80: 43–62. |
18. | Reibman, A., & Trivedi, K. (1988). Numerical transient analysis of Markov models. Computers & Operations Research, 15: 19-36. doi:10.1016/ 0305-0548(88)90026-3. |
19. | Stewart, W. J. (2009). Probability, Markov chains, queues, and simulation: the mathematical basis of performance modeling. Princeton University Press. |
20. | Van Moorsel, A. P., & Wolter, K. (1998). Numerical solution of nonhomogeneous Markov processes through uniformization. ESM: 710-717. |