Modeling a Hospital Queueing Network

Creemers, Stefan; Lambrecht, Marc

doi:10.1007/978-1-4419-6472-4_18

Cited by 15 publications

(5 citation statements)

References 47 publications

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“…In [22] hospital departments and their interdepartmental relationships are modeled as a network with G/G/s queues. Analysis of the network gives relevant information such as utilization rates and mean waiting times for each queue, and also allows for exploring the impact of service interruptions, aggregating patient flows, and determining the optimal number of patients in a clinic session.…”

Section: Other Applicationsmentioning

confidence: 99%

Queuing Networks in Health Care Systems

Zonderland

2011

International Series in Operations Research &Amp; Management Science

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Over the last decades, the concept of patient flow has received an increased amount of attention. Healthcare professionals have become aware that in order to analyze the performance of a single healthcare facility, its relationship with other healthcare facilities should also be taken into account. A natural choice for analysis of networks of healthcare facilities is queuing theory. With a queuing network a fast and flexible analysis is provided that discovers bottlenecks and allows for the evaluation of alternative setups of the network. In this chapter we describe how queuing theory, and networks of queues in particular, can be invoked to model, study, analyze and solve healthcare problems. We describe important theoretical queuing results, give a review of the literature on the topic, discuss in detail two examples of how a healthcare problem is analyzed using a queuing network, and suggest directions for future research.

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Section: Other Applicationsmentioning

confidence: 99%

Queuing Networks in Health Care Systems

Zonderland

2011

International Series in Operations Research &Amp; Management Science

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“…There is extensive literature on the applications of queueing network models to service systems. For example, see Sauer and Chandy (1981) for a review of applications in computer networks, see Banerjee et al (2015), Freund et al (2017) and Ozkan and Ward (2017) for examples in ride‐sharing economies and see Chan et al (2016), Creemers and Lambrecht (2011), Dai and Shi (2019), Kim et al (2018) and Zacharias and Armony (2016) for healthcare‐related applications.…”

Section: Introductionmentioning

confidence: 99%

A robust queueing network analyzer based on indices of dispersion

Whitt

You

2021

Naval Research Logistics

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This dissertation concludes five years of doctoral study and research. I am deeply indebted to many exceptional scholars and inspiring individuals, who guide me through an voyage of discovery that never meant to be a downwind sailing.First and foremost, I would like to thank my advisor, Professor Ward Whitt, for the enlightening guidance throughout my life as a Ph.D. student. He is always a walking encyclopedia to me with his accurate pointers to the literature and his prompt and constructive feedback. His generosity in sharing his knowledge from both the mathematics and engineering point of view helped me greatly in understanding the big picture of the subject matter of this work. As a great educator and scholar, he has been and will always be a role model that I look up to.

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“…There is an extensive literature on the applications of queueing network models to service systems. For example, see [39] for a review of applications in computer networks, see [3,17,36] for examples in ride-sharing economies and see [6,9,11,30,56] for healthcare-related applications.…”

Section: Introductionmentioning

confidence: 99%

A Robust Queueing Network Analyzer Based on Indices of Dispersion

Whitt

You

2020

Preprint

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We develop a robust queueing network analyzer algorithm to approximate the steady-state performance of a single-class open queueing network of single-server queues with Markovian routing. The algorithm allows non-renewal external arrival processes, general service-time distributions and customer feedback. We focus on the customer flows, defined as the continuous-time processes counting customers flowing into or out of the network, or flowing from one queue to another. Each flow is partially characterized by its rate and a continuous function that measures the stochastic variability over time. This function is a scaled version of the variance-time curve, called the index of dispersion for counts (IDC). The required IDC functions for the flows can be calculated from the model primitives, estimated from data or approximated by solving a set of linear equations. A robust queueing technique is used to generate approximations of the mean steady-state performance at each queue from the IDC of the total arrival flow and the service specification at that queue. The algorithm effectiveness is supported by extensive simulation studies and heavy-traffic limits.

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Modeling a Hospital Queueing Network

Cited by 15 publications

References 47 publications

Queuing Networks in Health Care Systems

Queuing Networks in Health Care Systems

A robust queueing network analyzer based on indices of dispersion

A Robust Queueing Network Analyzer Based on Indices of Dispersion

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