On the interaction between stratification and control variates, with illustrations in a call centre simulation

Variance reduction techniques (VRTs) are often essential to make simulation quick and accurate enough to be useful. A case in point is simulation-based optimization of complex systems. An obvious idea to push the improvement one step further is to combine several VRTs for a given simulation. But such combinations often give rise to new issues. This paper studies the combination of stratification with control variates. We detail and compare several ways of doing the combination. Nontrivial synergies between the two methods are exhibited. We illustrate this with a telephone call centre simulation, where we combine a control variate with stratification with respect to one of the uniform random variates that drive the simulation. It turns out that using more information in the control variate degrades the performance (significantly) in our example. This seemingly paradoxical behaviour is not rare and our theoretical analysis explains why.

[1]  C.-L. Sandblom,et al.  Cutting Plane Methods , 2000 .

[2]  Ward Whitt,et al.  Dynamic staffing in a telephone call center aiming to immediately answer all calls , 1999, Oper. Res. Lett..

[3]  Shane G. Henderson,et al.  Call Center Staffing with Simulation and Cutting Plane Methods , 2004, Ann. Oper. Res..

[4]  Pierre L'Ecuyer,et al.  A Java library for simulating contact centers , 2005, Proceedings of the Winter Simulation Conference, 2005..

[5]  Avishai Mandelbaum,et al.  Telephone Call Centers: Tutorial, Review, and Research Prospects , 2003, Manuf. Serv. Oper. Manag..

[6]  Pierre L'Ecuyer,et al.  Modeling Daily Arrivals to a Telephone Call Center , 2003, Manag. Sci..

[7]  Pierre L'Ecuyer,et al.  Simulation in Java with SSJ , 2005, Proceedings of the Winter Simulation Conference, 2005..

[8]  Pierre L'Ecuyer,et al.  Staffing Multiskill Call Centers via Linear Programming and Simulation , 2008, Manag. Sci..

[9]  James R. Wilson,et al.  Integrated Variance Reduction Strategies for Simulation , 1996, Oper. Res..

[10]  Pierre L'Ecuyer,et al.  Variance Reduction in the Simulation of Call Centers , 2006, Proceedings of the 2006 Winter Simulation Conference.

[11]  Stephen S. Lavenberg,et al.  A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations , 1981 .

[12]  A. Winsor Sampling techniques. , 2000, Nursing times.

[13]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[14]  P. L’Ecuyer,et al.  Variance Reduction via Lattice Rules , 1999 .

[15]  Russell C. H. Cheng,et al.  Variance reduction methods , 1986, WSC '86.

[16]  C. R. Deboor,et al.  A practical guide to splines , 1978 .

[17]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[18]  P. Glynn,et al.  Some New Perspectives on the Method of Control Variates , 2002 .

[19]  A. Owen,et al.  Control variates for quasi-Monte Carlo , 2005 .

[20]  Paul Bratley,et al.  A guide to simulation (2nd ed.) , 1986 .

[21]  Paul Bratley,et al.  A guide to simulation , 1983 .

[22]  Linus Schrage,et al.  A guide to simulation , 1983 .

[23]  Peter W. Glynn,et al.  Efficiency improvement techniques , 1994, Ann. Oper. Res..

[24]  Thomas E. Booth,et al.  Unbiased combinations of nonanalog Monte Carlo techniques and fair games , 1990 .