The Location of Emergency Service Facilities

Toregas, Constantine; Swain, Ralph W.; ReVelle, Charles; Bergman, Lawrence A.

doi:10.1287/opre.19.6.1363

Cited by 1,538 publications

(696 citation statements)

References 5 publications

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“…The goal is usually to maximize the fraction of accidents served within a certain (pre-determined) time. There are articles that search for the number of vehicles needed [17], the best base locations [4], and/or the best distribution of vehicles over the bases [5].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

An efficient heuristic for real-time ambulance redeployment

Jagtenberg

Bhulai

Mei

2015

Operations Research for Health Care

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“…The objective of the set-covering location problem (SCLP), first formulated by Toregas et al (1971), is to minimize the number of stations such that all demand points are covered. Although this is a binary problem, the LP relaxation (or the addition of a simple cutting plane) usually generates all-integer solutions.…”

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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“…Second, a distance measure is required and the distances from demand points to potential facilities is known. Third, the set of demand nodes constitutes the potential locations for facilities, meaning that facilities may only be placed on points of demand (Toregas et al, 1971(Toregas et al, , pg. 1364).…”

Section: Objective Functionsmentioning

confidence: 99%

“…If costs are identical for all possible facility locations, then an equivalent goal formulation would be minimizing the total number of facilities necessary to meet all demand. The solution will provide the locations as well as the number of facilities that offer the required service (Toregas et al, 1971). The definition of the location set covering model implies a guaranteed coverage, as every demand point must be within the maximal service distance to a facility.…”

Section: Maximal Covering Location and Location Set Covering Problemsmentioning

confidence: 99%