<b>OM Forum</b>—Offered Load Analysis for Staffing

Whitt, Ward

doi:10.1287/msom.1120.0428

Cited by 19 publications

(5 citation statements)

References 9 publications

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“…Liu and Whitt (2011b) analyze time-varying networks with many-server fluid queues and customer abandonment. In addition, time-varying queueing models have been analyzed for setting staffing requirements in service systems with unlimited queue capacity, by using the offered-load analysis (Whitt 2013). The methods for coping with time-varying demand when setting staffing levels are reviewed in Green et al (2007) and Whitt (2007).…”

Section: Queueing Network With Time-varying Parametersmentioning

confidence: 99%

Bed Blocking in Hospitals Due to Scarce Capacity in Geriatric Institutions—Cost Minimization via Fluid Models

Zychlinski

Mandelbaum

Momčilović

et al. 2020

M&SOM

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This research focuses on elderly patients who have been hospitalized, are ready to be discharged but must remain in the hospital until a bed in a geriatric institution becomes available; these patients "block" a hospital bed. Bed-blocking has become a challenge to healthcare operators due to its economic implications and quality-of-life effect on patients. Indeed, hospital-delayed patients, who cannot access their most appropriate treatment (e.g., rehabilitation), prevent new admissions. Moreover, bed-blocking is costly since a hospital bed is more expensive to operate than a geriatric bed. We are thus motivated to model and analyze the flow of patients between hospitals and geriatric institutions in order to improve their joint operation. 2. Academic/Practical Relevance: The joint modeling we suggest is necessary in order to capture blocking effects. In contrast to previous research, we address an entire time-varying network by explicitly considering blocking costs. Moreover, our fluid model captures blocking without the need for reflection, which simplifies the analysis as well as the convergence proof of the corresponding stochastic model. 3. Methodology: We develop a mathematical fluid model, which accounts for blocking, mortality and readmission-all significant features of the discussed environment. Then, for bed allocation decisions, we analyze the fluid model and its offered-load counterpart. 4. Results: The comparison between our fluid model, a two-year data set from a hospital chain and simulation results shows that our model is accurate. Moreover, our analysis yields a closed-form expression for bed allocation decisions, which minimizes the sum of underage and overage costs. Solving for the optimal number of geriatric beds in our system demonstrates that significant cost reductions are achievable, when compared to current operations. 5. Managerial Implications: Our model can help healthcare managers in allocating geriatric beds to reduce operational costs. Moreover, we offer two new capacity allocation approaches: a periodic reallocation of beds and the incorporation of setup cost into bed allocation decisions.

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Section: Queueing Network With Time-varying Parametersmentioning

confidence: 99%

Bed Blocking in Hospitals Due to Scarce Capacity in Geriatric Institutions—Cost Minimization via Fluid Models

Zychlinski

Mandelbaum

Momčilović

et al. 2020

M&SOM

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“…The general background and applicability of SRS is provided in Gans et al [6], Borst et al [170], Whitt [171], Koole and Mandelbaum [172]. Although theoretical and empirical evidence in support of the SRS rule has grown [61,109,173], the main challenge in practical applications lies in determining the appropriate value for the safety factor [37,61,170,174,175].…”

Section: Staffing Optimizationmentioning

confidence: 99%

Staffing and scheduling under nonstationary demand for service: A literature review

Defraeye

Nieuwenhuyse

2016

Omega

130

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Many service systems display nonstationary demand: the number of customers fluctuates over time according to a stochastic-though to some extent predictable-pattern. To safeguard the performance of such systems, adequate personnel capacity planning (i.e., determining appropriate staffing levels and/or shift schedules) is often crucial. This article provides a state-of-the-art literature review on staffing and scheduling approaches that account for nonstationary demand. Among references published during 1991-2013, it is possible to categorize relevant contributions according to system assumptions, performance evaluation characteristics, optimization approaches and real-life application contexts. Based on their findings, the authors develop recommendations for further research.

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“…The MOL approximation for delay was discussed in ,]. Short surveys of the MOL approach to staffing are given in .…”

Section: Staffing In Time‐varying Many‐server Loss Modelsmentioning

confidence: 99%

Many‐server loss models with non‐poisson time‐varying arrivals

Whitt

Zhao

2017

Naval Research Logistics

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This article proposes an approximation for the blocking probability in a many‐server loss model with a non‐Poisson time‐varying arrival process and flexible staffing (number of servers) and shows that it can be used to set staffing levels to stabilize the time‐varying blocking probability at a target level. Because the blocking probabilities necessarily change dramatically after each staffing change, we randomize the time of each staffing change about the planned time. We apply simulation to show that (i) the blocking probabilities cannot be stabilized without some form of randomization, (ii) the new staffing algorithm with randomiation can stabilize blocking probabilities at target levels and (iii) the required staffing can be quite different when the Poisson assumption is dropped. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 177–202, 2017

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OM Forum—Offered Load Analysis for Staffing

Cited by 19 publications

References 9 publications

Bed Blocking in Hospitals Due to Scarce Capacity in Geriatric Institutions—Cost Minimization via Fluid Models

Bed Blocking in Hospitals Due to Scarce Capacity in Geriatric Institutions—Cost Minimization via Fluid Models

Staffing and scheduling under nonstationary demand for service: A literature review

Many‐server loss models with non‐poisson time‐varying arrivals

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