Discrete-time conversion for simulating finite-horizon Markov processes
SIAM Journal on Applied Mathematics
Fast algorithms for generating discrete random variates with changing distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Proceedings of the 35th conference on Winter simulation: driving innovation
Approximating multi-skill blocking systems by hyperexponential decomposition
Performance Evaluation
A java library for simulating contact centers
WSC '05 Proceedings of the 37th conference on Winter simulation
A Staffing Algorithm for Call Centers with Skill-Based Routing
Manufacturing & Service Operations Management
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science
Hi-index | 0.00 |
Staffing and scheduling optimization in large multiskill call centers is time-consuming, mainly because it requires lengthy simulations to evaluate performance measures and their sensitivity. Simplified models that provide tractable formulas are unrealistic in general. In this paper we explore an intermediate solution, based on an approximate continuous-time Markov chain model of the call center. This model is more accurate than the commonly used approximations, and yet can be simulated faster than a more realistic simulation (based on non-exponential distributions and additional details). To speed up the simulation, we uniformize the Markov chain and simulate only its discrete-time version. We show how performance measures such as the fraction of calls of each type answered within a given waiting time limit can be recovered from this simulation, how to synchronize common random numbers in this setting, and how to use this in the first phase of an optimization algorithm based on the cutting plane method. We also discuss various implementation issues and provide empirical results.