Capacity Sizing Under Parameter Uncertainty: Safety Staffing Principles Revisited

Bassamboo, Achal; Randhawa, Ramandeep S.; Zeevi, Assaf

doi:10.1287/mnsc.1100.1203

Cited by 101 publications

(85 citation statements)

References 25 publications

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“…Extant literature either ignores absenteeism or treats it as an exogenous phenomenon (Bassamboo et al (2010), Easton and Goodale (2005), Harrison and Zeevi (2005) and Whitt (2006) provide examples of call-center staffing while Fry et al (2006) provide an example of firefighter staffing). The uncertain supply of service capacity created by nurse absenteeism connects our work with a stream of literature focused on inventory planning in the presence of unreliable supply/stochastic production yield (Yano and Lee (1995)) with two important distinctions.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“Nursevendor Problem”: Personnel Staffing in the Presence of Endogenous Absenteeism

2013

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The problem of determining nurse staffing levels in a hospital environment is a complex task due to variable patient census levels and uncertain service capacity caused by nurse absenteeism. In this paper we combine an empirical investigation of the factors affecting nurse absenteeism rates with an analytical treatment of nurse staffing decisions using a novel variant of the newsvendor model. Using data from the emergency department of a large urban hospital, we find that absenteeism rates are consistent with nurses exhibiting an aversion to higher levels of anticipated workload. Using our empirical findings we analyze a single-period nurse staffing problem considering both the case of constant absenteeism rate (exogenous absenteeism) as well as an absenteeism rate which is a function of the number of scheduled nurses (endogenous absenteeism).We provide characterizations of the optimal staffing levels in both situations and show that the failure to incorporate absenteeism as an endogenous effect results in understaffing.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

“Nursevendor Problem”: Personnel Staffing in the Presence of Endogenous Absenteeism

2013

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“…Few works used quantitative techniques to quantify risk in supply chain. Reference [19] Proposed MILP approach for selection supplier under risk management. Reference [20] used the same approach but they assume selection supplier depend on risk of late delivery.…”

Section: Literature Review Of Supplier Selection Under Risk Managmentioning

confidence: 99%

Supplier selection under disruption risks using Stochastic Mixed Linear Programming techniques

Hamdi¹,

Ghorbel

Masmoudi

2014

2014 International Conference on Advanced Logistics and Transport (ICALT)

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Supplier selection is an important key of supply chain management and mainly with the presence of disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. Many approaches have been developed in the literature based on various formal modeling techniques. In this paper, Stochastic Mixed Linear Program (MILP) techniques are used for the selection of suppliers under risk disruption. Two set of disruption scenarios are considered: (1) scenario with independent local disruption of each supplier, and (2) scenario with local and global disruption that may result in all suppliers simultaneously. The two percentiles: Value at risk (VaR) and conditional value at risk (CVaR) are used to model the risk of supply chain disruption. It be concluded that these percentiles are capable to optimizing the supply portfolio by minimizing expects worst-case per part via calculating the value at risk of expected cost part. The extension of this study seems very interesting for the risk analysis in complex supply chains.

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“…Starting from the classical Poisson process, it is common practice to increase dispersion by using a mixed Poisson process [2,13], to that end replacing a deterministic parameter λ by a random parameter Λ. This leads to the idea of modeling overdispersed arrival processes by a mixed version of NHPPs, so-called Cox processes [5], where the time-dependent rate λ(t) of the classical NHPP is replaced by a stochastic process Λ(t).…”

Section: Introductionmentioning

confidence: 99%

Scaling limits for infinite-server systems in a random environment

Heemskerk¹,

Leeuwaarden²,

Mandjes³

2017

Stoch. Syst.

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Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditionsThis article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org.The Publisher does not warrant or guarantee the article's accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. Copyright © 2017, The author(s)Please scroll down for article-it is on subsequent pages INFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. This paper studies the effect of an overdispersed arrival process on the performance of an infinite-server system. In our setup, a random environment is modeled by drawing an arrival rate Λ from a given distribution every Δ time units, yielding an i.i.d. sequence of arrival rates Λ1, Λ2, . . .. Applying a martingale central limit theorem, we obtain a functional central limit theorem for the scaled queue length process. We proceed to large deviations and derive the logarithmic asymptotics of the queue length's tail probabilities. As it turns out, in a rapidly changing environment (i.e., Δ is small relative to Λ) the overdispersion of the arrival process hardly affects system behavior, whereas in a slowly changing random environment it is fundamentally different; this general finding applies to both the central limit and the large deviations regime. We extend our results to the setting where each arrival creates a job in multiple infinite-server queues.

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Capacity Sizing Under Parameter Uncertainty: Safety Staffing Principles Revisited

Cited by 101 publications

References 25 publications

“Nursevendor Problem”: Personnel Staffing in the Presence of Endogenous Absenteeism

“Nursevendor Problem”: Personnel Staffing in the Presence of Endogenous Absenteeism

Supplier selection under disruption risks using Stochastic Mixed Linear Programming techniques

Scaling limits for infinite-server systems in a random environment

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