In consulting services, call centres serve several types of customers. The demand of different customers who ask for specific skills hinders the ability to balance the workload among servers; thus, task reallocation may be needed. In this paper, we model a call centre with multi-type customers as a multi-class tandem queue and examine how to reallocate the workload among servers. To evaluate the performance of the system, we propose an approximate analytical method based on the aggregation of Markov chain models. To optimally reallocate the workload, we propose a search algorithm based on the optimal computing budget allocation method. A simulation is employed to validate the analytical method and identify regions in which it can be successfully applied. A case study shows the applicability of the approach and quantifies its benefits in a realistic situation.

Modelling and workload reallocation of call centres with multi-type customers

Matta, Andrea
2017-01-01

Abstract

In consulting services, call centres serve several types of customers. The demand of different customers who ask for specific skills hinders the ability to balance the workload among servers; thus, task reallocation may be needed. In this paper, we model a call centre with multi-type customers as a multi-class tandem queue and examine how to reallocate the workload among servers. To evaluate the performance of the system, we propose an approximate analytical method based on the aggregation of Markov chain models. To optimally reallocate the workload, we propose a search algorithm based on the optimal computing budget allocation method. A simulation is employed to validate the analytical method and identify regions in which it can be successfully applied. A case study shows the applicability of the approach and quantifies its benefits in a realistic situation.
2017
markov modelling; optimisation; performance analysis; queueing systems; workload control; Strategy and Management1409 Tourism, Leisure and Hospitality Management; Management Science and Operations Research; Industrial and Manufacturing Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1048309
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