Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

Gans, Noah; Shen, Haipeng; Zhou, Yong Pin; Korolev, Nikolay; McCord, Alan; Ristock, Herbert

doi:10.1287/msom.2015.0546

Cited by 57 publications

(34 citation statements)

References 42 publications

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“…This approach has been considered in the emergency department setting (e.g. Peck et al (2012), Xu and Chan (2016)) and call centers (Gans et al 2015). In contrast to these prior works, we do not directly use the predicted probability of deterioration or death in the ward/TCU provided by the EDIP2 model.…”

Section: Related Literaturementioning

confidence: 99%

An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score

Chan

Zubizarreta

et al. 2018

M&SOM

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Unplanned transfers of patients from general medical-surgical wards to the Intensive Care Unit (ICU) can occur due to unexpected patient deterioration. Such patients tend to have higher mortality rates and longer lengths-of-stay than direct admissions to the ICU. As such, the medical community has invested substantial efforts in the development of patient risk scores with the intent to identify patients at risk of deterioration. In this work, we consider how one such risk score could be used to trigger proactive transfers to the ICU. We utilize a retrospective dataset from 21 Kaiser Permanente Northern California hospitals to estimate the potential benefit of transferring patients to the ICU at various levels of patient risk of deterioration. In order to reduce the sensitivity of our findings to key identification and modeling assumptions, we use a combination of multivariate matching and instrumental variable approaches. Using our empirical results to calibrate a simulation model, we find that proactively transferring the most severe patients could reduce mortality rates and lengthsof-stay without increasing other adverse events; however, proactive transfers should be used judiciously as being too aggressive could increase ICU congestion and degrade quality of care.

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Section: Related Literaturementioning

confidence: 99%

An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score

Chan

Zubizarreta

et al. 2018

M&SOM

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“…A key assumption of this multiplicative model is that β d i does not depend on the week number. Such a multiplicative model has been applied in several call center forecasting models (see Brown, Gans, Mandelbaum, Sakov, Shen, Zeltyn, & Zhao, 2005;Gans et al, 2009;Weinberg, Brown, & Stroud, 2007). Therefore, our estimation model changes to (6) where WF w i is the estimated number of fresh calls in week w i , WA w i and WC w i are the total number of abandoned calls and total number of connected calls in week w i , respectively.…”

Section: Intraweek Seasonalitymentioning

confidence: 99%

“…Ibrahim and L'Ecuyer (2013) added the correlation between different call types into a model with additive seasonality, interday correlation and intraday correlation. A multiplicative way to model the intraweek and intraday pattern was used by Gans, Shen, Zhou, Korolev, McCord, and Ristock (2009).…”

Section: Introductionmentioning

confidence: 99%

“…However, Hoffman and Harris (1986) only consider the redial behavior, and they neglect the reconnect behavior. Also, the fresh call arrival rate is assumed to be a constant among certain hours of the day in their model, whereas in most call centers the arrival rate is far from constant over the day, exhibiting certain intraday pattern, see (Gans et al, 2009;Ibrahim & L'Ecuyer, 2013;Shen & Huang, 2008). In this paper, we propose a queueing model that has two extra orbits than the Erlang C model, where abandoned customers redial via one orbit, and answered customers reconnect via the other orbit.…”

Section: Introductionmentioning

confidence: 99%

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On the estimation of the true demand in call centers with redials and reconnects

Ding

Koole²,

Mei

2015

European Journal of Operational Research

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a b s t r a c tIn 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.

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“…Gurvich et al (2010) use chance constraints for targets on long-term abandonment ratio for multi-skill centers. Gans et al (2012) optimize work shifts with recourse in a single-skill center.…”

Section: Introductionmentioning

confidence: 99%

Chance-constrained scheduling with recourse for multi-skill call centers with arrival-rate and absenteeism uncertainty

Ta¹,

Chan²,

L’Ecuyer³

et al. 2015

2015 Winter Simulation Conference (WSC)

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We consider a chance-constrained two-stage stochastic scheduling problem for multi-skill call centers with uncertainty on arrival rate and absenteeism. We first determine an initial schedule based on an imperfect forecast on arrival rate and absenteeism. Then, this schedule is corrected applying recourse actions when the forecast becomes more accurate in order to satisfy the service levels and average waiting times constraints with some predefined probabilities. We propose a method that combines simulation with integer programming and cut generation to solve the problem.

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Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

Cited by 57 publications

References 42 publications

An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score

An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score

On the estimation of the true demand in call centers with redials and reconnects

Chance-constrained scheduling with recourse for multi-skill call centers with arrival-rate and absenteeism uncertainty

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