Optimizing daily agent scheduling in a multiskill call center

Abstract

We examine and compare simulation-based algorithms for solving the agent scheduling problem in a multiskill call center. This problem consists in minimizing the total costs of agents under constraints on the expected service level per call type, per period, and aggregated. We propose a solution approach that combines simulation with integer or linear programming, with cut generation. In our numerical experiments with realistic problem instances, this approach performs better than all other 1 methods proposed previously for this problem. We also show that the two-step approach, which is the standard method for solving this problem, sometimes yield solutions that are highly suboptimal and inferior to those obtained by our proposed method.

Forecasting Call Center Arrivals: Fixed-Effects, Mixed-Effects, and Bivariate Models

“…Shen and Huang (2008b) also described the importance of using distributional forecasts for future arrival rates (see §3.2.2 of that paper). Distributional forecasts are needed, for example, in simulation-based algorithms that optimize the staffing and scheduling of agents, as in Cezik and L'Ecuyer (2008) and Avramidis et al (2010). If a distributional forecast is available for the arrival rate, then we can simulate by first generating the rate, then the arrivals from a Poisson process with that same rate.…”

“…Second, there is the problem of scheduling (and re-scheduling) the available pool of agents based on updated forecasts, typically made several days or weeks in advance. That is a problem of "resource deployment"; see Avramidis et al (2010).…”

Retail store scheduling for profit

“…We could think of a two-stage approach that computes the optimal staffing in a first stage and then finds a set of admissible shifts that cover these required staffings, say by having a number of salespersons larger or equal to the optimal number in each time period, at minimal cost. But such a procedure is generally suboptimal, as illustrated for example in Avramidis et al (2010) in the context of work schedule construction for telephone call centers.…”

A three-stage approach for aircraft line maintenance personnel rostering using MIP, discrete event simulation and DEA

“…This method is common in call center applications, where the workload depends on the distributions of call arrivals and durations (e.g., Henderson (2004, 2008); Avramidis, Chan, Gendreau, L'Ecuyer, and Pisacane (2010); Avramidis, Chan, and L'Ecuyer (2009)). …”