Prédiction du délai d'attente en temps réel et modélisation des durées de service dans les centres d'appels multi-compétences

In this thesis, we begin with the study of delay prediction of customers in multiskill call centers. Waiting time has an important impact on the quality of service experienced by customers.  Delay announcement can reduce customer uncertainty about its delay time. It also can increase customer satisfaction and reduce the number of abandonments. This requires having a good delay predictor. Unfortunately existing predictors are not adapted for multiskill call centers.  We propose three types of predictors that use machine learning: the first uses regression cubic splines, the second employs artificial neural networks, and the latter uses the stochastic kriging. The predictors take as inputs the delay of the last customer of the same type to enter service, the arrival period of the new customer, the staffing of agents groups, the queue length of the same type, and the queue lengths of types served by the same agents. These predictors work well for multiskill call centers, but one drawback is that they have a large number of parameters that must be learned in advance during the training phase that requires a large amount of data and computional time.  We also propose two new delay predictors that are very simple to implement, require little optimization effort,  do not need any data, and are applicable in multiskill call centers. They are based on the wait times of previous customers of the same class. The first one estimates the delay of a new customer by extrapolating the wait history of customers currently in queue, plus the delay of last one that started service, and taking a weighted average. The second one takes a weighted average of the delays of the past customers of the same class that have found the same queue length when they arrived.   Next in this thesis, we are also interested in modelling service time in call centers. In general, the standard Erlang queueing models are used to analyze call centers operations. In these models, agent service times are modelled as independent and identically distributed  exponential random variables with a constant mean. Several recent studies have shown that the distribution of service time is: time-dependent, lognormal rather than exponential, and distinct by agent.   We propose a more realistic modelling of service times in call centers that takes into account multiple properties observed in real life data. Our models take into account: the heterogeneity of agents, the time dependence, serial correlation between service time of an agent for the same call type, and the cross-correlations between several call types served by the same agent. We show that these models predict agent average service time better than the considered benchmark models. Thereafter, we show by simulation that these more realistic models lead to system performance predictions significantly different from those of the benchmark models, and decisions that manager could take by observing this data can lead to important cost savings in practice.

Keyphrases: Wait time, Machine learning, Delay history, Modelling, Service time, Simulation