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Emergency medical service (EMS) providers are charged with the task of managing ambulances so that the time required to respond to emergency calls is minimized. One approach that may assist in reducing response times is ambulance redeployment, i.e., repositioning idle ambulances in real time. We formulate a simulation model of EMS operations to evaluate the performance of a given allocation policy...
We estimate the probability that the game of Monopoly between two players playing very simple strategies never ends. Four different estimators, based respectively on straightforward simulation, a Brownian motion approximation, asymptotics for Markov chains, and importance sampling all yield an estimate of approximately twelve percent.
Suppose one wishes to compare two closely related systems via stochastic simulation. Common random numbers (CRN) involves using the same streams of uniform random variates as inputs for both systems to sharpen the comparison. One can view CRN as a particular choice of copula that gives the joint distribution of the inputs of both systems. We discuss the possibility of using more general copulae, including...
Continuous-variable simulation optimization problems are those optimization problems where the objective function is computed through stochastic simulation and the decision variables are continuous. We discuss verifiable conditions under which the objective function is continuous or differentiable, and outline some key properties of two classes of methods for solving such problems, namely sample-average...
We study the one-dimensional root finding problem for increasing convex functions. We give gradient-free algorithms for both exact and inexact (stochastic) function evaluations. For the stochastic case, we supply a probabilistic convergence guarantee in the spirit of selection-of-the-best methods. A worst-case bound on the work performed by the algorithm shows an improvement over naive stochastic...
We assume the existence of a parameterized family of control variates that could be used in a regenerative steady-state simulation. We show how such controls can be generated in the Markov-process setting, discuss the optimization problem of searching for a good choice of parameterization, and develop a strong law and central limit theorem for the resulting estimator.
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