Waiting time predictors for multi-skill call centers

Mamadou Thiongane 1 Wyean Chan 1 Pierre L'Ecuyer 2, 1
1 DIRO - Département d'Informatique et de Recherche Opérationnelle [Montreal]
2 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
Inria Rennes – Bretagne Atlantique , IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
Abstract : We develop customer delay predictors for multi-skill call centers that take into inputs the queueing state upon arrival and the waiting time of the last customer served. Many predictors have been proposed and studied for the single queue system, but barely any predictor currently exists for the multi-skill case. We introduce two new predictors that use cubic regression splines and artificial neural networks, respectively, and whose parameters are optimized (or learned) from observation data obtained by simulation. In numerical experiments, our proposed predictors are much more accurate than a popular heuristic that uses as a predictor the delay of the last customer of the same type that started service.
Type de document :
Communication dans un congrès
L. Yilmaz; M.K.V. Chan; I. Moon; T. M. K. Roeder; C. Macal; M.D. Rossetti. 2015 Winter Simulation Conference, Dec 2015, Huntington Beach, United States
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Mamadou Thiongane, Wyean Chan, Pierre L'Ecuyer. Waiting time predictors for multi-skill call centers. L. Yilmaz; M.K.V. Chan; I. Moon; T. M. K. Roeder; C. Macal; M.D. Rossetti. 2015 Winter Simulation Conference, Dec 2015, Huntington Beach, United States. 〈hal-01240150〉

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