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A Bound on the Performance of an Optimal Ambulance Redeployment Policy

Authors:

Matthew S. Maxwell,

Shane G. Henderson,

Huseyin Topaloglu,

Susan R. HunterAuthors Info & Claims

Operations Research, Volume 62, Issue 5

Pages 1014 - 1027

https://doi.org/10.1287/opre.2014.1302

Published: 01 October 2014 Publication History

Abstract

Ambulance redeployment is the practice of repositioning ambulance fleets in real time in an attempt to reduce response times to future calls. When redeployment decisions are based on real-time information on the status and location of ambulances, the process is called system-status management. An important performance measure is the long-run fraction of calls with response times over some time threshold. We construct a lower bound on this performance measure that holds for nearly any ambulance redeployment policy through comparison methods for queues. The computation of the bound involves solving a number of integer programs and then simulating a multiserver queue. This work originated when one of the authors was asked to analyze a response to a request-for-proposals RFP for ambulance services in a county in North America.

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Henderson S(2023)Practical Impact and Academia are not AntonymsProceedings of the Winter Simulation Conference10.5555/3643142.3643144(16-30)Online publication date: 10-Dec-2023
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Sanoubar Sde Jonge BMaillart LProkopyev O(2023)Optimal Condition-Based Maintenance via a Mobile Maintenance ResourceTransportation Science10.1287/trsc.2021.030257:6(1646-1670)Online publication date: 29-Aug-2023
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A Bound on the Performance of an Optimal Ambulance Redeployment Policy
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Published In

cover image Operations Research

Operations Research Volume 62, Issue 5

October 2014

232 pages

ISSN:0030-364X

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INFORMS

Linthicum, MD, United States

Publication History

Published: 01 October 2014

Accepted: 01 May 2014

Received: 01 July 2013

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Cited By

Henderson S(2023)Practical Impact and Academia are not AntonymsProceedings of the Winter Simulation Conference10.5555/3643142.3643144(16-30)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643144
Sanoubar Sde Jonge BMaillart LProkopyev O(2023)Optimal Condition-Based Maintenance via a Mobile Maintenance ResourceTransportation Science10.1287/trsc.2021.030257:6(1646-1670)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1287/trsc.2021.0302
Benjaafar SJiang DLi XLi X(2022)Dynamic Inventory Repositioning in On-Demand Rental NetworksManagement Science10.1287/mnsc.2021.428668:11(7861-7878)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1287/mnsc.2021.4286
Dai TTayur S(2020)OM Forum—Healthcare Operations ManagementManufacturing & Service Operations Management10.1287/msom.2019.077822:5(869-887)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1287/msom.2019.0778
He SSim MZhang M(2019)Data-Driven Patient Scheduling in Emergency DepartmentsManagement Science10.1287/mnsc.2018.314565:9(4123-4140)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1287/mnsc.2018.3145
Dayapule DRaghavan ATadepalli PFern A(2018)Emergency response optimization using online hybrid planningProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304665(4722-4728)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304665
Nasrollahzadeh AKhademi AMayorga M(2018)Real-Time Ambulance Dispatching and RelocationManufacturing & Service Operations Management10.1287/msom.2017.064920:3(467-480)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1287/msom.2017.0649
Hu ZHong L(2015)Robust simulation of stochastic systems with input uncertainties modeled by statistical divergencesProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888692(643-654)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888692

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