This paper 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 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.
Absfruet-The Run by Run Controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The Run by Run Controller combines the advantages of both Statistical moCess Control (SPC) and Feedback Control. It has three components: Rapid Mode, Gradual Mode, and Generalized SPC. Rapid mode adapts to sudden shifts in the process such as those caused by maintenance operations. Gradual mode adapts to gradual drifts in the process such as those caused by build-up of deposition inside a reactor. The choice between the two modes is determined by the outcome from Generalized SPC which allows SPC to be applied to a process while it is being tuned. The Run by Run Controller has been applied to the control of a silicon epitaxy process in a barrel reactor. Rapid mode recovered the process within 3 runs after a disturbance. Gradual mode reduced the variation of the process by a factor of 2.7 as compared to historical data.
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.
W e compare the performance of seven methods in computing or approximating service levels for nonstationary M t /M/s t queueing systems: an exact method (a Runge-Kutta ordinary-differential-equation solver), the randomization method, a closure (or surrogate-distribution) approximation, a direct infinite-server approximation, a modified-offered-load infinite-server approximation, an effective-arrival-rate approximation, and a lagged stationary approximation. We assume an exhaustive service discipline, where service in progress when a server is scheduled to leave is completed before the server leaves. We used all of the methods to solve the same set of 640 test problems. The randomization method was almost as accurate as the exact method and used about half the computational time. The closure approximation was less accurate, and usually slower, than the randomization method. The two infinite-server-based approximations, the effective-arrival-rate approximation, and the lagged stationary approximation were less accurate but had computation times that were far shorter and less problem-dependent than the other three methods.
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