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Operations research methods for optimization in radiation oncology

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Operations research methods for optimization in radiation oncology. / Ehrgott, Matthias; Holder, Allen.
In: Journal of Radiation Oncology Informatics , Vol. 6, No. 1, 2014, p. 1-41.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Ehrgott, M & Holder, A 2014, 'Operations research methods for optimization in radiation oncology', Journal of Radiation Oncology Informatics , vol. 6, no. 1, pp. 1-41. https://doi.org/10.5166/jroi-6-1-21

APA

Ehrgott, M., & Holder, A. (2014). Operations research methods for optimization in radiation oncology. Journal of Radiation Oncology Informatics , 6(1), 1-41. https://doi.org/10.5166/jroi-6-1-21

Vancouver

Ehrgott M, Holder A. Operations research methods for optimization in radiation oncology. Journal of Radiation Oncology Informatics . 2014;6(1):1-41. doi: 10.5166/jroi-6-1-21

Author

Ehrgott, Matthias ; Holder, Allen. / Operations research methods for optimization in radiation oncology. In: Journal of Radiation Oncology Informatics . 2014 ; Vol. 6, No. 1. pp. 1-41.

Bibtex

@article{44b61085ab014b8f824e33203a1fbf00,
title = "Operations research methods for optimization in radiation oncology",
abstract = "Operations Research has a successful tradition of applying mathematical analysis to a wide range of applications, and problems in Medical Physics have been popular over the last couple of decades. The original application was in the optimal design of the fluence map for a radiotherapy treatment, a problem that has continued to receive attention. However, Operations Research has been applied to other clinical problems like patient scheduling, vault design, and image alignment. The overriding theme of this article is to present how techniques in Operations Research apply to clinical problems, which we accomplish in three parts. First, we present the perspective from which an operations researcher addresses a clinical problem. Second, we succinctly introduce the underlying methods that are used to optimize a system, and third, we demonstrate how modernsoftware facilitates problem design. Our discussion is supported by several publications to foster continued study. With numerous clinical, medical, and managerial decisions associated with a clinic, operations research has a promising future at improving how radiotherapy treatments are designed and delivered.",
keywords = "Optimization, Mathematical Programming, Radiotherapy Design, Linear Programming, Operations Research",
author = "Matthias Ehrgott and Allen Holder",
year = "2014",
doi = "10.5166/jroi-6-1-21",
language = "English",
volume = "6",
pages = "1--41",
journal = "Journal of Radiation Oncology Informatics ",
publisher = "Bern Open Publishing",
number = "1",

}

RIS

TY - JOUR

T1 - Operations research methods for optimization in radiation oncology

AU - Ehrgott, Matthias

AU - Holder, Allen

PY - 2014

Y1 - 2014

N2 - Operations Research has a successful tradition of applying mathematical analysis to a wide range of applications, and problems in Medical Physics have been popular over the last couple of decades. The original application was in the optimal design of the fluence map for a radiotherapy treatment, a problem that has continued to receive attention. However, Operations Research has been applied to other clinical problems like patient scheduling, vault design, and image alignment. The overriding theme of this article is to present how techniques in Operations Research apply to clinical problems, which we accomplish in three parts. First, we present the perspective from which an operations researcher addresses a clinical problem. Second, we succinctly introduce the underlying methods that are used to optimize a system, and third, we demonstrate how modernsoftware facilitates problem design. Our discussion is supported by several publications to foster continued study. With numerous clinical, medical, and managerial decisions associated with a clinic, operations research has a promising future at improving how radiotherapy treatments are designed and delivered.

AB - Operations Research has a successful tradition of applying mathematical analysis to a wide range of applications, and problems in Medical Physics have been popular over the last couple of decades. The original application was in the optimal design of the fluence map for a radiotherapy treatment, a problem that has continued to receive attention. However, Operations Research has been applied to other clinical problems like patient scheduling, vault design, and image alignment. The overriding theme of this article is to present how techniques in Operations Research apply to clinical problems, which we accomplish in three parts. First, we present the perspective from which an operations researcher addresses a clinical problem. Second, we succinctly introduce the underlying methods that are used to optimize a system, and third, we demonstrate how modernsoftware facilitates problem design. Our discussion is supported by several publications to foster continued study. With numerous clinical, medical, and managerial decisions associated with a clinic, operations research has a promising future at improving how radiotherapy treatments are designed and delivered.

KW - Optimization

KW - Mathematical Programming

KW - Radiotherapy Design

KW - Linear Programming

KW - Operations Research

U2 - 10.5166/jroi-6-1-21

DO - 10.5166/jroi-6-1-21

M3 - Journal article

VL - 6

SP - 1

EP - 41

JO - Journal of Radiation Oncology Informatics

JF - Journal of Radiation Oncology Informatics

IS - 1

ER -