Validating and applying a model for locating emergency medical vehicles in Tuczon, AZ

Goldberg, Jeffrey B.; Dietrich, Robert A.; Chen, Jen Ming; Mitwasi, Mousa George; Valenzuela, Terry; Criss, Elizabeth A.

doi:10.1016/0377-2217(90)90402-w

Cited by 103 publications

(41 citation statements)

References 26 publications

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“…Over the years, several variants of MEXCLP have been published by different authors [7], [15]. These models are generally considered to give good static solutions.…”

Section: Static Modelsmentioning

confidence: 99%

An efficient heuristic for real-time ambulance redeployment

Jagtenberg

Bhulai

Mei

2015

Operations Research for Health Care

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We address the problem of dynamic ambulance repositioning, in which the goal is to minimize the expected fraction of late arrivals. The decisions on how to redeploy the vehicles have to be made in real time, and may take into account the status of all other vehicles and accidents. This is generally considered a difficult problem, especially in urban areas, and exact solution methods quickly become intractable when the number of vehicles grows. Therefore, there is a need for a scalable algorithm that performs well in practice.We propose a polynomial-time heuristic that distinguishes itself by requiring neither assumptions on the region nor extensive state information. We evaluate its performance in a simulation model of emergency medical services (EMS) operations. We compare the performance of our repositioning method to so-called static solutions: a classical scenario in which an idle vehicle is always sent to its predefined base location. We show that the heuristic performs better than the optimal static solution for a tractable problem instance. Moreover, we perform a realistic urban case study in which we show that the performance of our heuristic is a 16.8% relative improvement on a benchmark static solution. The studied problem instances show that our algorithm fulfils the need for real-time, simple redeployment policies that significantly outperform static policies.

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“…Over the years, several variants of MEXCLP have been published by different authors [7], [15]. These models are generally considered to give good static solutions.…”

Section: Static Modelsmentioning

confidence: 99%

An efficient heuristic for real-time ambulance redeployment

Jagtenberg

Bhulai

Mei

2015

Operations Research for Health Care

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“…Perhaps the paper that is most relevant to ours is Goldberg and Paz (1991), which is inspired by a case study reported in Goldberg et al (1990a) and Goldberg et al (1990b). They formulate an emergency facility location model that includes the probability P ij that an ambulance at station i can travel to a call from demand node j within a response time standard.…”

Section: Literaturementioning

confidence: 99%

Optimal ambulance location with random delays and travel times

Ingolfsson

Budge

Erkut

2008

Health Care Manage Sci

215

102

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We describe an optimization model for ambulance location that maximizes the expected system wide coverage, given a total number of ambulances. The model measures expected coverage as the fraction of calls reached within a given time standard and considers response time to be composed of a random delay (prior to travel to the scene) plus a random travel time. Pre-travel delays at dispatch and activation stages can be significant, and models that do not account for such delays can severely overestimate the possible coverage for a given number of ambulances and underestimate the number of ambulances needed to provide a specified coverage level. By explicitly modeling the randomness in the delays and the travel time, we arrive at a more realistic model for ambulance location. In order to capture the dependence of ambulance busy fractions on the allocation of ambulances between stations, we iterate between solving the optimization model and using the approximate hypercube model to calculate busy fractions. We illustrate the use of the model using actual data from Edmonton.

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“…The hypercube queuing model is used to calculate the server's busy fractions. Goldberg et al [30] developed a model to locate emergency facilities. The goal of their model is to maximize the expected number of customers who are responded to within eight minutes (success rate).…”

Section: Single Dispatch Total Backup and Homogeneous Serversmentioning

confidence: 99%

“…Goldberg and Paz [49] modified the model presented in Goldberg et al [30] by considering the FIFO queue discipline, customer classification and allocation of multiple servers at each base location. They tested the applicability of the model in more real test problems and proposed a heuristic algorithm.…”

Section: Single Dispatch Total Backup and Non-homogeneous Serversmentioning

confidence: 99%

“…Additionally, environmental measures can be regarded.  Due to the significance of travel time as a portion of service time, many studies have tried to analyze this time more accurately [12,18,30,41]; however, travel time is always evaluated as a part of the service time. Therefore, it can be very helpful to define a measure to examine the travel time solely.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

2018

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Abstract. This study provides a review of hypercube queuing models (HQMs) in emergency service systems (ESSs). This survey presents a comprehensive review and taxonomy of models, solutions and applications related to the HQM after Larson [12]. In addition, the structural aspects of HQMs are examined. Important contributions of the existing research are addressed by taking into account multiple factors. In particular, the integration of location decisions with HQMs for designing an ESS is discussed. Finally, a list of issues for future studies are presented.

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Validating and applying a model for locating emergency medical vehicles in Tuczon, AZ

Cited by 103 publications

References 26 publications

An efficient heuristic for real-time ambulance redeployment

An efficient heuristic for real-time ambulance redeployment

Optimal ambulance location with random delays and travel times

Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

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