Poisson and non-Poisson properties in appointment-generated arrival processes: The case of an endocrinology clinic

Song Hee Kim; Ponni Vel; Ward Whitt; Won Chul Cha

doi:10.1016/j.orl.2015.02.008

Poisson and non-Poisson properties in appointment-generated arrival processes: The case of an endocrinology clinic

Song Hee Kim, Ponni Vel, Ward Whitt, Won Chul Cha

Department of Emergency Medicine

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

Previous statistical tests showed that call center arrival data were consistent with a non-homogeneous Poisson process (NHPP) within each day, but exhibit over-dispersion over multiple days. These tests are applied to arrival data from an endocrinology clinic, where arrivals are by appointment. The clinic data are also consistent with an NHPP within each day, but exhibit under-dispersion over multiple days. This analysis supports a new Gaussian-uniform arrival process model, with Gaussian daily totals and uniformly distributed arrivals given the totals.

Original language	English
Pages (from-to)	247-253
Number of pages	7
Journal	Operations Research Letters
Volume	43
Issue number	3
DOIs	https://doi.org/10.1016/j.orl.2015.02.008
State	Published - May 2015

Keywords

Appointments
Dispersion
Fitting queueing models to data
Queues with scheduled arrivals
Statistical tests Poisson processes

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10.1016/j.orl.2015.02.008

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title = "Poisson and non-Poisson properties in appointment-generated arrival processes: The case of an endocrinology clinic",

abstract = "Previous statistical tests showed that call center arrival data were consistent with a non-homogeneous Poisson process (NHPP) within each day, but exhibit over-dispersion over multiple days. These tests are applied to arrival data from an endocrinology clinic, where arrivals are by appointment. The clinic data are also consistent with an NHPP within each day, but exhibit under-dispersion over multiple days. This analysis supports a new Gaussian-uniform arrival process model, with Gaussian daily totals and uniformly distributed arrivals given the totals.",

keywords = "Appointments, Dispersion, Fitting queueing models to data, Queues with scheduled arrivals, Statistical tests Poisson processes",

author = "Kim, \{Song Hee\} and Ponni Vel and Ward Whitt and Cha, \{Won Chul\}",

year = "2015",

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doi = "10.1016/j.orl.2015.02.008",

language = "English",

volume = "43",

pages = "247--253",

journal = "Operations Research Letters",

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T1 - Poisson and non-Poisson properties in appointment-generated arrival processes

T2 - The case of an endocrinology clinic

AU - Kim, Song Hee

AU - Vel, Ponni

AU - Whitt, Ward

AU - Cha, Won Chul

PY - 2015/5

Y1 - 2015/5

N2 - Previous statistical tests showed that call center arrival data were consistent with a non-homogeneous Poisson process (NHPP) within each day, but exhibit over-dispersion over multiple days. These tests are applied to arrival data from an endocrinology clinic, where arrivals are by appointment. The clinic data are also consistent with an NHPP within each day, but exhibit under-dispersion over multiple days. This analysis supports a new Gaussian-uniform arrival process model, with Gaussian daily totals and uniformly distributed arrivals given the totals.

AB - Previous statistical tests showed that call center arrival data were consistent with a non-homogeneous Poisson process (NHPP) within each day, but exhibit over-dispersion over multiple days. These tests are applied to arrival data from an endocrinology clinic, where arrivals are by appointment. The clinic data are also consistent with an NHPP within each day, but exhibit under-dispersion over multiple days. This analysis supports a new Gaussian-uniform arrival process model, with Gaussian daily totals and uniformly distributed arrivals given the totals.

KW - Appointments

KW - Dispersion

KW - Fitting queueing models to data

KW - Queues with scheduled arrivals

KW - Statistical tests Poisson processes

UR - https://www.scopus.com/pages/publications/84924937015

U2 - 10.1016/j.orl.2015.02.008

DO - 10.1016/j.orl.2015.02.008

M3 - Article

AN - SCOPUS:84924937015

SN - 0167-6377

VL - 43

SP - 247

EP - 253

JO - Operations Research Letters

JF - Operations Research Letters

IS - 3

ER -

Poisson and non-Poisson properties in appointment-generated arrival processes: The case of an endocrinology clinic

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Keywords

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