all centers are complex systems in which it is essential to optimize the trade-off between the service level provided to the customers and the cost for the personnel. In this paper we describe a quantitative approach to choose the most suitable contracts to hire the call center operators. The aim is to organize their work-shifts and their rest periods, including lunch-breaks, in such a way that the mix of skills obtained in each time slot is as close as possible to a desired level, estimated according to demand forecasts. The approach here proposed is based on a heuristic method which exploits a general purpose linear programming solver.
This paper deals with the problem of finding the most suitable contracts to be used when hiring the operators of a call center and deciding their optimal working schedule, to optimize the trade-off between the service level provided to the customers and the cost of the personnel. In a previous paper (Cordone et al. 2011), we proposed a heuristic method to quickly build an integer solution from the solution of the continuous relaxation of an integer linear programming model. In this paper, we generalize that model to take into account a much wider class of working contracts, allowing heterogeneous shift patterns, as well as legal constraints related to continuously active working environments. Since our original rounding heuristic cannot be extended to the new model, due to its huge size and to the involved correlations between different sets of integer variables, we introduce a more sophisticated heuristic based on decomposition and on a multi-level iterative structure. We compare the results of this heuristic with those of a Greedy Randomized Adaptive Search Procedure, both on real-world instances and on realistic random instances.
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