A joint chance-constrained programming approach for call center workforce scheduling under uncertain call arrival forecasts. (June 2016)

• Record Type: Journal Article
• Authors: Excoffier, M.; Gicquel, C.; Jouini, O.
• Is Part Of: Computers & industrial engineering. Volume 96(2016)
• Page Start: 16
• Page End: 30
• Publication Date: 2016-06
• Subjects: Personnel planning -- Call center shift scheduling -- Customer abandonment -- Stochastic programming -- Probabilistic constraints -- Mixed-integer linear programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.03.013
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We study the call center shift scheduling problem under uncertain demand forecasts. Forecasting errors are seen as independent normally distributed random variables. The resulting stochastic problem is modeled as a joint chance-constrained program. A mixed-integer linear programming based solution approach is proposed. Numerical results based on a real case study and managerial insights are provided. Abstract: We consider a workforce management problem arising in call centers, namely the shift-scheduling problem. It consists in determining the number of agents to be assigned to a set of predefined shifts so as to optimize the trade-off between manpower cost and customer quality of service. We focus on explicitly taking into account in the shift-scheduling problem the uncertainties in the future call arrival rates forecasts. We model them as independent random variables following a continuous probability distribution. The resulting stochastic optimization problem is handled as a joint chance-constrained program and is reformulated as an equivalent large-size mixed-integer linear program. One key point of the proposed solution approach is that this reformulation is achieved without resorting to a scenario generation procedure to discretize the continuous probability distributions. Our computational results show that the proposed approach can efficiently solve real-size instances of the problem, enabling us to draw some useful managerial insights on the underlying risk-cost trade-off.