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Quantifying the preparedness for efficient ambulance logistics
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Preparedness is an important concept in ambulance logistics. It is a way of evaluating the ability to serve potential patients with ambulances in a swift and efficient manner. Here, a way of calculating the preparedness for different parts of a geographical area is presented. The developed measure is calibrated for use in the county of Stockholm in Sweden. Different areas of application for the measure are discussed in strategic, tactical and operational ambulance planning. Specifically, a mathematical model for ambulance station location, based on the preparedness measure, is developed and solved using a simulated annealing heuristic. A new set of locations, obtained from the model, is compared to the existing locations by simulating the ambulance operations. The results show that the new locations have a positive effect on the patient waiting periods.

Keyword [en]
Preparedness, Ambulance Logistics, Location Analysis, Optimization, Transportation
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-85912OAI: oai:DiVA.org:liu-85912DiVA: diva2:573708
Available from: 2012-12-03 Created: 2012-12-03 Last updated: 2012-12-03
In thesis
1. Decision support tools for dynamic fleet management: applications in airline planning and ambulance logistics
Open this publication in new window or tab >>Decision support tools for dynamic fleet management: applications in airline planning and ambulance logistics
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this work, decision support tools for dynamic fleet management problems are developed. The operational management tasks include dispatching, supervising and sometimes reconstructing the vehicle schedule. These tasks are often complex, while the time available to complete them is short. Therefore, decision support tools assisting the controller can be of great service.

Two studies on dynamic fleet management problems are presented. The first concerns airline planning and the problem of how to handle schedule perturbations. The second study is within the field of ambulance logistics, with focus on operational ambulance control.

For an airline, the flight perturbation problem arises when it is impossible to continue executing the original aircraft schedule, e.g. due to an aircraft breakdown. The flight controller then has to decide whether some flights have to be delayed or cancelled, or perhaps flights have to be reassigned to new aircraft. This is done in order to find an attainable, cost effective solution, where the airline can continue to serve the passengers. In this work, a decision support tool that can repair the aircraft schedule is suggested. The tool is based on a multicommodity network flow model with side constraints. A number of different solution strategies are suggested for the model, including heuristics based on column generation and meta heuristics utilizing path relinking. Computational tests show that several of the solution algorithms find near optimal solutions within a few seconds.

Ambulance logistics comprises most aspects of managing an efficient ambulance health care. This involves both emergency medical services and patient transportations. Controlling a fleet of ambulances includes deciding which ambulance to dispatch to a call. The controller also has to ensure that most or all parts of the area of responsibility can be reached by an ambulance in a short time. This is referred to as maintaining an adequate level of preparedness in the area. In this work, a way of calculating the preparedness is presented. It is incorporated into a number of decision support tools, including a dispatch tool and a relocation tool. The dispatch tool can suggest which ambulance to assign to a new call. The relocation tool suggests a way of relocating the ambulances, i.e. sending them to new locations, to increase the level of preparedness in the area. A simulation tool is also developed and used to study the effect of dynamically relocating ambulances. Furthermore, it is used to test if new locations for the ambulance stations will affect the waiting periods for the patients. The results show that the decision support tools have a positive effect on the system performance.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. 64 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 942
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-28405 (URN)13541 (Local ID)91-85297-91-7 (ISBN)13541 (Archive number)13541 (OAI)
Public defence
2005-05-20, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 13:15 (English)
Opponent
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-10-15Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
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  • sv-SE
  • Other locale
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Output format
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