10 research outputs found
A method for estimation of redial and reconnect probabilities in call centers
In practice, many call center forecasters use the total inbound volume to make forecasts. In reality, besides the fresh calls (initial call attempts), there are many redials (re-attempts after abandonments) and reconnects (re-attempts after answered calls) in call centers. Neglecting redials and reconnects will inevitably lead to inaccurate forecasts, which eventually leads to inaccurate staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh. Motivated by this, the goal of this paper is to estimate the number of fresh calls, and the redial and reconnect probabilities. To this end, we propose a model to estimate these three variables. We formulate our estimation model as a minimization problem, where the actual redial and reconnect probabilities lead to the minimum objective value. We validate our estimation results via real call center data and simulated data
On the estimation of the true demand in call centers with redials and reconnects
In practice, in many call centers customers often perform redials (i.e., reattempt after an abandonment) and reconnects (i.e., reattempt after an answered call). In the literature, call center models usually do not cover these features, while real data analysis and simulation results show ignoring them inevitably leads to inaccurate estimation of the total inbound volume. Therefore, in this paper we propose a performance model that includes both features. In our model, the total volume consists of three types of calls: (1) fresh calls (i.e., initial call attempts), (2) redials, and (3) reconnects. In practice, the total volume is used to make forecasts, while according to the simulation results, this could lead to high forecast errors, and subsequently wrong staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh and what fractions of the calls are redials and reconnects.
Motivated by this, we propose a model to estimate the number of fresh calls, and the redial and reconnect probabilities, using real call center data that has no customer-identity information. We show that these three variables cannot be estimated simultaneously. However, it is empirically shown that if one variable is given, the other two variables can be estimated accurately with relatively small bias. We show that our estimations of redial and reconnect probabilities and the number of fresh calls are close to the real ones, both via real data analysis and simulation
Fluid Approximation of a Call Center Model with Redials and Reconnects
In many call centers, callers may call multiple times. Some of the calls are
re-attempts after abandonments (redials), and some are re-attempts after
connected calls (reconnects). The combination of redials and reconnects has not
been considered when making staffing decisions, while ignoring them will
inevitably lead to under- or overestimation of call volumes, which results in
improper and hence costly staffing decisions. Motivated by this, in this paper
we study call centers where customers can abandon, and abandoned customers may
redial, and when a customer finishes his conversation with an agent, he may
reconnect. We use a fluid model to derive first order approximations for the
number of customers in the redial and reconnect orbits in the heavy traffic. We
show that the fluid limit of such a model is the unique solution to a system of
three differential equations. Furthermore, we use the fluid limit to calculate
the expected total arrival rate, which is then given as an input to the Erlang
A model for the purpose of calculating service levels and abandonment rates.
The performance of such a procedure is validated in the case of single
intervals as well as multiple intervals with changing parameters
A Note on an M/M/s Queueing System with two Reconnect and two Redial Orbits
A queueing system with two reconnect orbits, two redial (retrial) orbits, s servers and two independent Poisson streams of customers is considered. An arriving customer of type i, i = 1, 2 is handled by an available server, if there is any; otherwise, he waits in an infinite buffer queue. A waiting customer of type i who did not get connected to a server will lose his patience and abandon after an exponentially distributed amount of time, the abandoned one may leave the system (lost customer) or move into one of the redial orbits, from which he makes a new attempt to reach the primary queue, and when a customer finishes his conversation with a server, he may comeback to the system, to one of the reconnect orbits where he will wait for another service. In this paper, a fluid model is used to derive a first order approximation for the number of customers in the redial and reconnect orbits in the heavy traffic. The fluid limit of such a model is a unique solution to a system of three differential equations
A method for estimation of redial and reconnect probabilities in call centers
In practice, many call center forecasters use the total inbound volume to make forecasts. In reality, besides the fresh calls (initial call attempts), there are many redials (re-attempts after abandonments) and reconnects (re-attempts after answered calls) in call centers. Neglecting redials and reconnects will inevitably lead to inaccurate forecasts, which eventually leads to inaccurate staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh. Motivated by this, the goal of this paper is to estimate the number of fresh calls, and the redial and reconnect probabilities. To this end, we propose a model to estimate these three variables. We formulate our estimation model as a minimization problem, where the actual redial and reconnect probabilities lead to the minimum objective value. We validate our estimation results via real call center data and simulated data.
A method for estimation of redial and reconnect probabilities in call centers
In practice, many call center forecasters use the total inbound volume to make forecasts. In reality, besides the fresh calls (initial call attempts), there are many redials (re-attempts after abandonments) and reconnects (re-attempts after answered calls) in call centers. Neglecting redials and reconnects will inevitably lead to inaccurate forecasts, which eventually leads to inaccurate staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh. Motivated by this, the goal of this paper is to estimate the number of fresh calls, and the redial and reconnect probabilities. To this end, we propose a model to estimate these three variables. We formulate our estimation model as a minimization problem, where the actual redial and reconnect probabilities lead to the minimum objective value. We validate our estimation results via real call center data and simulated data
The medicalization of deviance in China
äŗę“²ēÆē½ŖåøåøęConference Theme: Asian Innovations in Criminology and Criminal JusticePart 5: Juvenile Delinquency and JusticeConrad and Schneiderās now classical work on the historical transformation of definitions of deviance from ābadnessā to āsicknessā is relevant for the situation in China today, although with some modifications. The weakly founded medical/psychiatric profession and the strong political/ideological discourse in China leads to a strange combination of medicalization and moralization, even criminalization of deviance. The āsickā is often combined with the ābadā, and āsicknessā is often seen as a secondary sign of ābadnessā. The pan-moralist tradition of ancient China seems to be closely combined with the Communist eraās strong belief in political-ideological correctness, and its strong belief in social engineering. It is interesting to note that my research on crime and deviance in China in the 1980s and 1990s seems to be confirmed by todayās discourse, although there are new moral panics and new forms of medical-moralistic definitions of deviance in China today. Still, the categories of deviance are very much socially constructed entities closely related to the moral-political order of present day China. I will use three cases to underline my argument. First, the type of deviance I call āmajority devianceā, related to the case of the prejudice and dangers associated with the only-child. My second example has to do with what I term the āwayward girlā and the moral panics concerning so-called zaolian ā or āpremature loveā among young girls. The third example is the new panic surrounding āinternet addiction disorderā or IAD. While the ādiscoā and the ādance hallā were the sites of disorder in the 1980s and 90s, the wangba ā or āinternet barā is now seen as the most dangerous site of crime and deviance.postprin