Peakedness‐Based Staffing for Call Center Outsourcing

This study considers the staffing problem of a vendor call center in a co‐sourcing setting. The aim is to take short‐term variability and correlations in time for call arrivals at such a vendor call center into account. To do so, peakedness is proposed as a useful measure of the burstiness in the arrival stream. The study empirically demonstrates the presence of bursty arrivals at a call center and proposes an approach to the measurement of the peakedness of the arrival stream making use of standard call center data. The problematic nature of bursty arrivals in the context of call center co‐sourcing is demonstrated along with an asymptotic result establishing that the problem persists in large call centers. The study then analyzes two peakedness‐based staffing methods: one which is a well known extension of the square root staffing rule and another which makes use of the Hayward approximation principles. Both approaches are simple and enable the vendor to improve its staffing procedure with good accuracy.     

Nested Kriging predictions for datasets with a large number of observations

This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often considered to tackle such a problem, but the method suffers from its computational burden when the number of observation points is large. We introduce in this article nested Kriging predictors which are constructed by aggregating sub-models based on subsets of observation points. This approach is proven to have better theoretical properties than other aggregation methods that can be found in the literature. Contrarily to some other methods it can be shown that the proposed aggregation method is consistent. Finally, the practical interest of the proposed method is illustrated on simulated datasets and on an industrial test case with \(10^4\) observations in a 6-dimensional space.     

A review of Rankian will therapy and its relevance for social casework

Conclusion In this study I have postulated on teh basis of Rankian philosophy and my own personal and professional experience that 1) self-knowledge in and of itself rarely heals; 2) a client who is accepted by another, in his totality, is able to change; and 3) persons have an inherent need to become fate-determining beings.Engaging the other in a process of willing his own growth is the central aim and way of helping. Thus, the caseworker is not a foe, educator, or passive on-looker; rather, he lendshimself, to be used or not as the client chooses. There is in the caseworker a willingness to let the client fumble, flounder, and waver until his rigid Will can soften enough to encompass and use another. A caseworker who can permit this freedom offers his steadiness as a springboard against and with whom the patient finds his own strength. This process is characterized by a slow coming together, a period of union, and a separation when the newly-won self fears, yet seeks autonomy. Having discovered that he can unite with another without being destroyed, the client leaves, not cured, yet less restricted, more open to the promises of life.     

Optimizing the Staffing and Routing of Small‐Size Hierarchical Call Centers

Multiple‐skill call centers propagate rapidly with the development of telecommunications. An abundance of literature has already been published on call centers. Here, we want to focus on centers that would typically occur in business‐to‐business environments; these are call centers that handle many types of calls but where the arrival rate for each type is low. To find an optimal configuration, the integrality of the decision variables is a much more important issue than for larger call centers. The present paper proposes an approach that uses elements of combinatorial optimization to find optimal configurations. We develop an approximation method for the evaluation of the service performance. Next, we search for the minimum‐cost configuration subject to service‐level constraints using a branch‐and‐bound algorithm. What is at stake is to find the right balance between gains resulting from the economies of scale of pooling and the higher cost or cross‐trained agents. The article shows that in most cases this method significantly decreases the staffing cost compared with configurations with only cross‐trained or dedicated operators.