Browsing by Author "Ingolfsson, A."

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    ArticlePublicationOpen Access
    Ambulance location for maximum survival
    (Wiley, 2008-02) Erkut, Erhan; Ingolfsson, A.; Erdoğan, G.; Business Administration; ERKUT, Erhan
    This article proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an emergency medical service (EMS) vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures—the expected number of survivors in case of cardiac arrests. The survival-maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival-maximizing models using data from the Edmonton EMS system.
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    Approximating vehicle dispatch probabilities for emergency service systems with location-specific service times and multiple units per location
    (Informs, 2009-02) Budge, S.; Ingolfsson, A.; Erkut, Erhan; Business Administration; ERKUT, Erhan
    To calculate many of the important performance measures for an emergency response system, one requires knowledge of the probability that a particular server will respond to an incoming call at a particular location. Estimating these "dispatch probabilities" is complicated by four important characteristics of emergency service systems. We discuss these characteristics and extend previous approximation methods for calculating dispatch probabilities to account for the possibilities of workload variation by station, multiple vehicles per station, call- and station-dependent service times, and server cooperation and dependence.
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    ArticlePublicationOpen Access
    Computational comparison of five maximal covering models for locating ambulances
    (Wiley, 2009-01) Erkut, Erhan; Ingolfsson, A.; Sim, T.; Erdoğan, Güneş; Business Administration; Industrial Engineering; ERKUT, Erhan; ERDOĞAN, Güneş
    This article categorizes existing maximum coverage optimization models for locatingambulances based on whether the models incorporate uncertainty about (1) ambulanceavailability and (2) response times. Data from Edmonton, Alberta, Canada are used to test five different models, using the approximate hypercube model to compare solution quality between models. The basic maximum covering model, which ignores these two sources of uncertainty, generates solutions that perform far worse than those generated by more sophisticated models. For a specified number of ambulances, a model that incorporates both sources of uncertainty generates a configuration that covers up to 26% more of the demand than the configuration produced by the basic model.
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    ArticlePublicationOpen Access
    Optimal ambulance location with random delays and travel times
    (Springer Science+Business Media, 2008-09) Ingolfsson, A.; Budge, S.; Erkut, Erhan; Business Administration; ERKUT, Erhan
    We describe an ambulance location optimization model that minimizes the number of ambulances needed tonprovide a specified service level. The model measures service level 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. In addition to modeling the uncertainty in the delay and in the travel time, we incorporate uncertainty in the ambulance availability in determining the response time. Models that do not account for the uncertainty in all three of these components may overestimate the possible service level for a given number of ambulances and underestimate the number of ambulances needed to provide a specified service level. By explicitly modeling the randomness in the ambulance availability and in the delays and the travel times, we arrive at a more realistic ambulance location model. Our model is tractable enough to be solved with general-purpose optimization solvers for cities with populations around one Million. We illustrate the use of the model using actual data from Edmonton.
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    ArticlePublicationOpen Access
    Scheduling ambulance crews for maximum coverage
    (Palgrave MacMillan, 2010-04) Erdoğan, Güneş; Erkut, Erhan; Ingolfsson, A.; Laporte, G.; Business Administration; Industrial Engineering; ERDOĞAN, Güneş; ERKUT, Erhan
    This paper addresses the problem of scheduling ambulance crews in order to maximize the coverage throughout a planning horizon. The problem includes the subproblem of locating ambulances to maximize expected coverage with probabilistic response times, for which a tabu search algorithm is developed. The proposed tabu search algorithm is empirically shown to outperform previous approaches for this subproblem. Two integer programming models that use the output of the tabu search algorithm are constructed for the main problem. Computational experiments with real data are conducted. A comparison of the results of the models is presented.