@incollection{henmas04,
	abstract = {
The ambulance-planning problem includes operational decisions such as choice of dispatching policy, strategic decisions such as where ambulances should be stationed and at what times they should operate, and tactical decisions such as station location selection. Any solution to this problem requires careful balancing of political, economic and medical objectives. Quantitative decision processes are becoming increasingly important in providing public accountability for the resource decisions that have to be made. We discuss a simulation and analysis software tool `BARTSIM' that was developed as a decision support tool for use within the St. John Ambulance Service (Auckland Region) in New Zealand (St. Johns). The novel features incorporated within this study include
- the use of a detailed time-varying travel model for modelling travel times in the simulation,
- methods for reducing the computational overhead associated with computing time-dependent shortest paths in the travel model,
- the direct reuse of real data as recorded in a database (trace-driven simulation), and
- the development of a geographic information sub-system (GIS) within BARTSIM that provides spatial visualisation of both historical data and the results of what-if simulations.
Our experience with St. Johns, and discussions with emergency operators in Australia, North America, and Europe, suggest that emergency services do not have good tools to support their operations management at all levels (operational, strategic and tactical). Our experience has shown that a customized system such as BARTSIM can successfully combine GIS and simulation approaches to provide a quantitative decision support tool highly valued by management. Further evidence of the value of our system is provided by the recent selection of BARTSIM by the Metropolitan Ambulance Service for simulation of their operations in Melbourne, Australia. This work has led to the development of BARTSIM's successor, SIREN (Simulation for Improving Response times in Emergency Networks), which includes many enhancements to handle the greater complexities of the Melbourne operations.},
	address = {Boston},
	author = {S. G. Henderson and A. J. Mason},
	booktitle = {Operations Research and Health Care: A Handbook of Methods and Applications},
	date-added = {2016-01-10 16:07:54 +0000},
	date-modified = {2016-01-10 16:07:54 +0000},
	editor = {M.~L.\ Brandeau and F.\ Sainfort and W. P.\ Pierskalla},
	pages = {77--102},
	publisher = {Kluwer Academic},
	title = {Ambulance service planning: simulation and data visualization},
	url_paper = {pubs/HendersonMasonChap.pdf},
	year = 2004}

{"_id":"oZnrjErhDdL4zKMmt","bibbaseid":"henderson-mason-ambulanceserviceplanningsimulationanddatavisualization-2004","downloads":1,"creationDate":"2016-01-10T16:41:19.310Z","title":"Ambulance service planning: simulation and data visualization","author_short":["Henderson, S. G.","Mason, A. J."],"year":2004,"bibtype":"incollection","biburl":"https://people.orie.cornell.edu/shane/ShanePubs.bib","bibdata":{"bibtype":"incollection","type":"incollection","abstract":"The ambulance-planning problem includes operational decisions such as choice of dispatching policy, strategic decisions such as where ambulances should be stationed and at what times they should operate, and tactical decisions such as station location selection. Any solution to this problem requires careful balancing of political, economic and medical objectives. Quantitative decision processes are becoming increasingly important in providing public accountability for the resource decisions that have to be made. We discuss a simulation and analysis software tool `BARTSIM' that was developed as a decision support tool for use within the St. John Ambulance Service (Auckland Region) in New Zealand (St. Johns). The novel features incorporated within this study include - the use of a detailed time-varying travel model for modelling travel times in the simulation, - methods for reducing the computational overhead associated with computing time-dependent shortest paths in the travel model, - the direct reuse of real data as recorded in a database (trace-driven simulation), and - the development of a geographic information sub-system (GIS) within BARTSIM that provides spatial visualisation of both historical data and the results of what-if simulations. Our experience with St. Johns, and discussions with emergency operators in Australia, North America, and Europe, suggest that emergency services do not have good tools to support their operations management at all levels (operational, strategic and tactical). Our experience has shown that a customized system such as BARTSIM can successfully combine GIS and simulation approaches to provide a quantitative decision support tool highly valued by management. Further evidence of the value of our system is provided by the recent selection of BARTSIM by the Metropolitan Ambulance Service for simulation of their operations in Melbourne, Australia. This work has led to the development of BARTSIM's successor, SIREN (Simulation for Improving Response times in Emergency Networks), which includes many enhancements to handle the greater complexities of the Melbourne operations.","address":"Boston","author":[{"firstnames":["S.","G."],"propositions":[],"lastnames":["Henderson"],"suffixes":[]},{"firstnames":["A.","J."],"propositions":[],"lastnames":["Mason"],"suffixes":[]}],"booktitle":"Operations Research and Health Care: A Handbook of Methods and Applications","date-added":"2016-01-10 16:07:54 +0000","date-modified":"2016-01-10 16:07:54 +0000","editor":[{"firstnames":["M. L.\\"],"propositions":[],"lastnames":["Brandeau"],"suffixes":[]},{"firstnames":["F.\\"],"propositions":[],"lastnames":["Sainfort"],"suffixes":[]},{"firstnames":["W.","P.\\"],"propositions":[],"lastnames":["Pierskalla"],"suffixes":[]}],"pages":"77–102","publisher":"Kluwer Academic","title":"Ambulance service planning: simulation and data visualization","url_paper":"pubs/HendersonMasonChap.pdf","year":"2004","bibtex":"@incollection{henmas04,\n\tabstract = {\nThe ambulance-planning problem includes operational decisions such as choice of dispatching policy, strategic decisions such as where ambulances should be stationed and at what times they should operate, and tactical decisions such as station location selection. Any solution to this problem requires careful balancing of political, economic and medical objectives. Quantitative decision processes are becoming increasingly important in providing public accountability for the resource decisions that have to be made. We discuss a simulation and analysis software tool `BARTSIM' that was developed as a decision support tool for use within the St. John Ambulance Service (Auckland Region) in New Zealand (St. Johns). The novel features incorporated within this study include\n- the use of a detailed time-varying travel model for modelling travel times in the simulation,\n- methods for reducing the computational overhead associated with computing time-dependent shortest paths in the travel model,\n- the direct reuse of real data as recorded in a database (trace-driven simulation), and\n- the development of a geographic information sub-system (GIS) within BARTSIM that provides spatial visualisation of both historical data and the results of what-if simulations.\nOur experience with St. Johns, and discussions with emergency operators in Australia, North America, and Europe, suggest that emergency services do not have good tools to support their operations management at all levels (operational, strategic and tactical). Our experience has shown that a customized system such as BARTSIM can successfully combine GIS and simulation approaches to provide a quantitative decision support tool highly valued by management. Further evidence of the value of our system is provided by the recent selection of BARTSIM by the Metropolitan Ambulance Service for simulation of their operations in Melbourne, Australia. This work has led to the development of BARTSIM's successor, SIREN (Simulation for Improving Response times in Emergency Networks), which includes many enhancements to handle the greater complexities of the Melbourne operations.},\n\taddress = {Boston},\n\tauthor = {S. G. Henderson and A. J. Mason},\n\tbooktitle = {Operations Research and Health Care: A Handbook of Methods and Applications},\n\tdate-added = {2016-01-10 16:07:54 +0000},\n\tdate-modified = {2016-01-10 16:07:54 +0000},\n\teditor = {M.~L.\\ Brandeau and F.\\ Sainfort and W. 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