Time-Dependent Ambulance Deployment and Shift Scheduling of Crews

Lara Wiesche ()
Additional contact information
Lara Wiesche: Ruhr University Bochum

A chapter in Operations Research Proceedings 2015, 2017, pp 591-596 from Springer

Abstract: Abstract For patients requesting emergency medical services (EMS) in a life-threatening emergency, the probability of survival is strongly related to the rapidness of assistance. An especially challenging task for planners is to allocate limited resources while managing increasing demand for services. The provision of sufficient staff resources for the ambulances has great impact on the initial treatment of patients and thus on the quality of emergency services. Data-driven empirically required ambulance location planning as well as the allocation of staff for these vehicles are successively optimized in the proposed approach to support emergency medical service decision makers. According to the identified problem structure, an integer linear programming model is proposed. An exemplary case study based on real-world data demonstrates how this approach can be used within the emergency medical service planning process.

Keywords: Emergency Medical Service; Demand Node; Integer Linear Programming Model; Shift Schedule; Emergency Medical Service System (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-42902-1_80

Ordering information: This item can be ordered from
http://www.springer.com/9783319429021

DOI: 10.1007/978-3-319-42902-1_80

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().