More on using forced idle time to improve performance in polling models

Probability in the Engineering and Informational Sciences

Volumn 13, Issue 4, 1999, Pages 489-

  Peköz, Erol A ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 25544478624     PISSN: 02699648     EISSN: 14698951     Source Type: Journal    
DOI: 10.1017/S0269964899134053     Document Type: Article

References (10)

1. Boxma, O.J. & Groenendijk, W.P. (1987). Pseudo-conservation laws in cyclic-service systems. Journal of Applied Probability 24: 949–964.
2. Cooper, R.B., Niu, S., & Srinivasan, M.M. (1998). When does forced idle time improve performance in polling models? Management Science 44(8): 1079–1086.
3. Konheim, A.G. & Levy, H. (1994). Descendent set: An efficient approach for the analysis of polling systems. IEEE Transactions on Communications 42: 1245–1252.
4. Sarkar, D. & Zangwill, W.I. (1991). Variance effects in cyclic production systems. Management Science 37(4): 443–453.
5. Tagaki, H. (1986). Analysis of polling systems. Cambridge: MIT Press.
6. Tagaki, H. (1994). Queuing analysis of polling models: Progress in 1990–1993. In J.H. Dshalalow (ed.), Frontiers in queuing models, methods and problems. Boca Raton: CRC Press.
7. Van Der Mei, R.D. & Levy, H. (1998). Expected delay analysis of polling systems in heavy traffic. Advances in Applied Probability 30: 586–602.
8. Wolff, R. (1989). Stochastic modeling and the theory of queues. Englewood Cliffs: Prentice-Hall.
9. Zangwill, W.I. (1992). The limits on Japanese production theory. Interfaces 22(5): 14–25.
10. Zangwill, W.I. & Sarkar, D. (1994). Response to comments on our work by Duenyas, by Gerchak and Zhang, and by McIntyre. Interfaces 24(5): 90–94.