"A Scalable Method to solve the Call Center Staffing with Service-Level Agreement under Uncertainty"
Gianmaria Leo, Renzo Benavente Sotelo, Julio C. Casas Quiroz and Victor Terpstra

The call center staffing is a complex planning problem that many firms deal with. The entire decision-making process usually aims to achieve the right trade-off between cost efficiency and a proper workforce planning that ensures the committed service level. This practice turns out to be challenging due to the presence of relevant stochastic factors impacting decisions, like arrival and duration of calls, waiting time and abandonment. Another important challenge arises from the restricted lead time to deliver the staff plans, whereas common solution approaches are usually computationally intensive or require exhaustive testing to validate assumptions and parameters. Our work focuses on a call center line managed by a major Bank of Peru. We introduced an effective simheuristic leading to a scalable framework that handles configurable assumptions. We validated our method by using a benchmark tool adopted by the Bank, and we compared it with a more typical approach adopted in production. The new method provides comparable solutions in terms of quality and accuracy, by reducing the computational time from hours to few minutes.