This paper introduces a new method for shift scheduling in multiskill call centers. The method consists of two steps. First, staffing levels are determined, and next, in the second step, the outcomes are used as input for the scheduling problem. The scheduling problem relies on a linear programming model that is easy to implement and has short computation times, i.e., a fraction of a second. Therefore, it is useful for different purposes and it can be part of an iterative procedure: for example, one that combines shifts into rosters.contact centers, multiskill call centers, shift scheduling, skill-based routing, staffing, workforce management
We study a simple method for staffing in multiskill call centers. The method has short computation times and determines nearly optimal staffing levels. It is in both views competitive to other methods from the literature. Because of the fast and accurate performance of the method, many different scenarios can be analyzed, and our method can be used for both tactical and strategic capacity management decisions.contact centers, multiskill call centers, skill-based routing, staffing, work force management
We consider an inbound call center with a fixed reward per call and communication and agent costs. By controlling the number of lines and the number of agents we can maximize the profit. Abandonments are included in our performance model. Monotonicity results for the maximization problem are obtained, which leads to an efficient optimization procedure. We give a counterexample to the concavity in the number of agents, which is equivalent to saying that the law of diminishing returns does not hold. Numerical results are given.
We consider a call center with two classes of impatient customers: premium and regular classes. Modeling our call center as a multiclass GI/GI/s + M queue, we focus on developing scheduling policies that satisfy a target ratio constraint on the abandonment probabilities of premium customers to regular ones. The problem is inspired by a real call center application in which we want to reach some predefined preference between customer classes for any workload condition. The motivation for this constraint comes from the difficulty of predicting in a quite satisfying way the workload. In such a case, the traditional routing problem formulation with differentiated service levels for different customer classes would be useless. For this new problem formulation, we propose two families of online scheduling policies: queue joining and call selection policies. The principle of our policies is that we adjust their routing rules by dynamically changing their parameters. We then evaluate the performance of these policies through a numerical study. The policies are characterized by simplicity and ease of implementation.
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