Coping with Time‐Varying Demand When Setting Staffing Requirements for a Service System

Green, Linda V.; Kolesar, Peter; Whitt, Ward

doi:10.1111/j.1937-5956.2007.tb00164.x

Cited by 243 publications

(239 citation statements)

References 106 publications

(187 reference statements)

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“…From day to day, the actual number of open or staffed beds slightly fluctuates (due to illness, holiday and patient demand) (Green et al 2007). Note that the number of physical bed positions is not necessarily equal to the number of operational beds; for most wards the number of physical beds is larger.…”

Section: Length Of Staymentioning

confidence: 99%

Dimensioning hospital wards using the Erlang loss model

Bekker²,

et al. 2009

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How many beds must be allocated to a specific clinical ward to meet production targets? When budgets get tight, what are the effects of downsizing a nursing unit? These questions are often discussed by medical professionals, hospital consultants, and managers. In these discussions the occupancy rate is of great importance and often used as an input parameter. Most hospitals use the same target occupancy rate for all wards, often 85%. Sometimes an exception is made for critical care and intensive care units. In this paper we demonstrate that this equity assumption is unrealistic and that it might result in an excessive number of refused admissions, particularly for smaller units. Queuing theory is used to quantify this impact. We developed a decision support system, based on the Erlang loss model, which can be used to evaluate the current size of nursing units. We validated this model with hospital data over the years [2004][2005][2006]. Finally, we demonstrate the efficiency of merging departments.

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Section: Length Of Staymentioning

confidence: 99%

Dimensioning hospital wards using the Erlang loss model

Bekker²,

et al. 2009

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“…al [4], Jennings et al [22], Green et al [12] or Fieldman et al [9]. Determining buffer capacity has been studied as well; see Harris et al [20], Whitaker [30] as well as Wallace [29] and [27].…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Call Center Design via Lagrangian Mechanics

Hampshire

Jennings

Massey

2009

Prob. Eng. Inf. Sci.

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We consider a multi-server delay queue with finite additional waiting spaces and time-varying arrival rates, where the customers waiting in the buffer may abandon. These are features that arise naturally from the study of service systems such as call centers. Moreover, we assume rewards for successful service completions and cost rates for service resources. Finally, we consider service level agreements that constrain both the fractions callers who abandon and the ones who are blocked.Applying the theory of Lagrangian mechanics to the fluid limit of a related Markovian service network model, we obtain near profit-optimal staffing and provisioning schedules. The nature of this solution consists of three modes of operation. A key step in deriving this solution is combining the modified offered load approximation for loss systems with our fluid model. We use them to estimate effectively both our service level agreement metrics and the profit for the original queueing model. Second-order profit improvements are achieved through a modified offered load version of the conventional square root safety rule.

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“…The problem we discuss is How can we set appropriate staffing levels in the face of such time-varying demand? Our discussion here is an abridged version of our recent survey in Green et al [9], including recent research in Feldman et al [5].…”

Section: Introductionmentioning

confidence: 98%