Automating warm-up length estimation

Hoad, Kathryn; Robinson, Stewart; Davies, Ruth

doi:10.1057/jors.2009.87

Cited by 51 publications

(33 citation statements)

References 38 publications

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“…Warm‐up times are used in simulation for the model to reach an optimal operating state. For this model, a warm‐up period of 41 days was established using Welch's method (Hoad, Robinson, & Davies, ); averages across shifts were taken for missed care, task in queue time, task queue and cumulative distance walked .…”

Section: Methodsmentioning

confidence: 99%

Predicting the effect of nurse–patient ratio on nurse workload and care quality using discrete event simulation

et al. 2019

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Aim A novel nurse‐focused discrete event simulation modelling approach was tested to predict nurse workload and care quality. Background It can be challenging for hospital managers to quantify the impact of changing operational policy and technical design such as nurse–patient ratios on nurse workload and care quality. Planning tools are needed—discrete event simulation is a potential solution. Method Using discrete event simulation, a demonstrator “Simulated Care Delivery Unit” model was created to predict the effects of varying nurse–patient ratios. Modelling inputs included the following: patient care data (GRASP systems data), inpatient unit floor plan and operating logic. Model outputs included the following: nurse workload in terms of task‐in‐queue, cumulative distance walked and Care quality in terms of task in queue time, missed care. Results The model demonstrated that as NPR increases, care quality deteriorated (120% missed care; 20% task‐in‐queue time) and nursing workload increased (120% task‐in‐queue; 110% cumulative walking distance). Conclusions DES has the potential to be used to inform operational policy and technical design decisions, in terms of impacts on nurse workload and care quality. Implications for Nursing Management This research offers the ability to quantify the impacts of proposed policy changes and technical design decisions, and provide a more cost‐effective and safe alternative to the current trial and error methodologies.

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Section: Methodsmentioning

confidence: 99%

Predicting the effect of nurse–patient ratio on nurse workload and care quality using discrete event simulation

et al. 2019

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“…Hence, when increasing τ from 0 to a small value, then the integral of the covariances will hardly increase. However, 1 T Àτ does increase, and for small enough t 1 , this will offset the increase of the covariance integral. This explains equation (8), which implies that Var( R(τ, t)) will always increase when τ is increased from 0 to some positive value.…”

Section: R(n)mentioning

confidence: 98%

“…The question now arises as to what is a good value for τ, a question that has been addressed in many papers. For a review of these methods, see Hoad et al, 1 Pasupathy and Schmeiser 2 and Pawlikowski. 3 To describe the problem more precisely, let us assume that the person conducting the simulation is interested in a certain random variable R, say a queue length, and this variable assumes the value R(t) at time t. In many cases, the expectation of R(t), E(R(t)) converges to an equilibrium value E(R), and the objective is to estimate E(R).…”

Section: Introductionmentioning

confidence: 99%

Factors affecting warm-up periods in discrete event simulation

Grassmann

2013

SIMULATION

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In this paper, we discuss the factors affecting the initialization bias in discrete event simulation. Specifically, we assume that the time average is used to find the equilibrium expectation of a certain variable R, say the number in a queueing network, and we would like to minimize the mean squared error (MSE) between the time average of R and its equilibrium expectation. To do this, a warm-up period is often used during which no data is collected, and we want to find the length of this period such that the MSE is minimal. We show that if starting in what Tocher calls a ''typical condition'', warm-ups tend to be redundant. This result is strengthened by theoretical arguments and numerical experiments. If starting in a typical state is inconvenient, warm-up periods should be used, and methods to find optimal warm-up periods are discussed. The numerical methods used for our experiments do not rely on Monte Carlo simulation. Instead, we determine the MSE of the time average by the randomization method and other deterministic methods.

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“…Since McClarnon (1990), various papers and theses (White and Minnox 1994, Rossetti, Delaney, and White 1995, White 1995, White 1997, Spratt 1998, White, Cobb, and Spratt 2000, Franklin and White 2008, Hoad, Robinson, and Davies 2008, White and Robinson 2009and White and Franklin 2010 have analyzed and advocated initial-transient algorithms based on the MSER statistic.…”

Section: Mser Aglorithmsmentioning

confidence: 99%

The initial transient in steady-state point estimation: Contexts, a bibliography, the mse criterion, and the MSER statistic

Pasupathy¹,

Schmeiser²

2010

Proceedings of the 2010 Winter Simulation Conference

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The initial transient is an unavoidable issue when estimating parameters of steady-state distributions. We discuss contexts and factors that affect how the initial transient is handled, provide a bibliography (from the system simulation literature), discuss criteria for evaluating initial-transient algorithms, arguing for focusing on the mean squared error (mse). We discuss the MSER statistic, showing that it is asymptotially proportional to the mse and therefore a good foundation for initial-transient algorithms. We suggest two new algorithms (MSER-LLM and MSER-LLM2) for using the MSER statistic and compare them, based on empirical results for M/M/1 and AR(1) data processes, to the original MSER algorithm (MSER-GM).

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Automating warm-up length estimation

Cited by 51 publications

References 38 publications

Predicting the effect of nurse–patient ratio on nurse workload and care quality using discrete event simulation

Predicting the effect of nurse–patient ratio on nurse workload and care quality using discrete event simulation

Factors affecting warm-up periods in discrete event simulation

The initial transient in steady-state point estimation: Contexts, a bibliography, the mse criterion, and the MSER statistic

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