Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming

► We investigated dispatching and relocation decisions of emergency service providers. ► ADP is powerful in solving the underlying stochastic and dynamic optimization problem. ► Average response time can be improved by using more flexible dispatching rules. ► Relocating ambulances proactively improv...

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Vydané v:	European journal of operational research Ročník 219; číslo 3; s. 611 - 621
Hlavný autor:	Schmid, Verena
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 16.06.2012 Elsevier Elsevier Sequoia S.A North-Holland Pub. Co
Predmet:	Ambulance location Ambulance services Applied sciences Approximate dynamic programming Approximation Biological and medical sciences Dynamic programming Economy. Management Emergency vehicles Exact sciences and technology Health and social institutions Mathematical programming Medical sciences Operational research and scientific management Operational research. Management science Optimization algorithms OR in health services Public health. Hygiene Public health. Hygiene-occupational medicine Stochastic optimization Studies Austria Ambulance location Approximate dynamic programming Emergency vehicles Stochastic optimization OR in health services Dispatching problem Operations research Medical transport Transit time Modeling Location problem Optimization Time dependence Emergency department Ambulance Dynamic programming Localization Mathematical programming Travel time Hospital management Empirical method Routing Real time Stochastic programming Response time Time average Emergency Service Proposal
ISSN:	0377-2217, 1872-6860
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Abstract	► We investigated dispatching and relocation decisions of emergency service providers. ► ADP is powerful in solving the underlying stochastic and dynamic optimization problem. ► Average response time can be improved by using more flexible dispatching rules. ► Relocating ambulances proactively improves service quality. ► Essential to take into account time-dependent information. Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests’ site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions.
AbstractList	Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests' site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions. ► We investigated dispatching and relocation decisions of emergency service providers. ► ADP is powerful in solving the underlying stochastic and dynamic optimization problem. ► Average response time can be improved by using more flexible dispatching rules. ► Relocating ambulances proactively improves service quality. ► Essential to take into account time-dependent information. Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests’ site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions. ► We investigated dispatching and relocation decisions of emergency service providers. ► ADP is powerful in solving the underlying stochastic and dynamic optimization problem. ► Average response time can be improved by using more flexible dispatching rules. ► Relocating ambulances proactively improves service quality. ► Essential to take into account time-dependent information. Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests’ site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions. Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests' site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions. [PUBLICATION ABSTRACT] Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests' site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions.Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests' site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions.
Author	Schmid, Verena
AuthorAffiliation	Department of Business Administration, University of Vienna, Bruenner Strasse 72, 1210 Vienna, Austria
AuthorAffiliation_xml	– name: Department of Business Administration, University of Vienna, Bruenner Strasse 72, 1210 Vienna, Austria
Author_xml	– sequence: 1 givenname: Verena surname: Schmid fullname: Schmid, Verena email: verena.schmid@univie.ac.at organization: Department of Business Administration, University of Vienna, Bruenner Strasse 72, 1210 Vienna, Austria
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Keywords	Ambulance location Approximate dynamic programming Emergency vehicles Stochastic optimization OR in health services Dispatching problem Operations research Medical transport Transit time Modeling Location problem Optimization Time dependence Emergency department Ambulance Dynamic programming Localization Mathematical programming Travel time Hospital management Empirical method Routing Real time Stochastic programming Response time Time average Emergency Service Proposal
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Snippet	► We investigated dispatching and relocation decisions of emergency service providers. ► ADP is powerful in solving the underlying stochastic and dynamic... Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two...
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SubjectTerms	Ambulance location Ambulance services Applied sciences Approximate dynamic programming Approximation Biological and medical sciences Dynamic programming Economy. Management Emergency vehicles Exact sciences and technology Health and social institutions Mathematical programming Medical sciences Operational research and scientific management Operational research. Management science Optimization algorithms OR in health services Public health. Hygiene Public health. Hygiene-occupational medicine Stochastic optimization Studies
Title	Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming
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