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The stochastic root-finding problem is that of finding a zero of a vector-valued function known only through a stochastic simulation. The simulation-optimization problem is that of locating a real-valued function's minimum, again with only a stochastic simulation that generates function estimates. Retrospective approximation (RA) is a sample-path technique(More)
We study control variate estimation where the control mean itself is estimated. Control variate estimation in simulation experiments can significantly increase sampling efficiency, and has traditionally been restricted to cases where the control has a known mean. In a previous paper (Schmeiser, Taaffe, and Wang 2000), we generalized the idea of control(More)
Dilemma zone (DZ), also called decision zone in other literature, is an area where drivers face an indecisiveness of stopping or crossing at the yellow onset. The DZ issue is a major reason for the crashes at high-speed signalized intersections. As a result, how to prevent approaching vehicles from being caught in the DZ is a widely concerning issue. In(More)
We consider the context of " stochastic recursions, " that is, recursions that involve quantities needing to be estimated using a stochastic simulation. Examples include certain stochastic adaptations of fixed-point recursions, line search recursions, and trust-region recursions obtained by replacing function and derivative values appearing within the(More)
We propose a testbed of simulation-optimization problems. The purpose of the testbed is to encourage development and constructive comparison of simulation-optimization techniques and algorithms. We are particularly interested in increasing attention to the finite-time performance of algorithms, rather than the asymptotic results that one often finds in the(More)
Consider the context of selecting an optimal system from among a finite set of competing systems, based on a " stochastic " objective function and subject to multiple " stochastic " constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero.(More)
The stochastic root-finding problem (SRFP) is that of finding the zero(s) of a vector function, that is, solving a nonlinear system of equations when the function is expressed implicitly through a stochastic simulation. SRFPs are equivalently expressed as stochastic fixed-point problems, where the underlying function is expressed implicitly via a noisy(More)
We present SimOpt --- a library of simulation-optimization problems intended to spur development and comparison of simulation-optimization methods and algorithms. The library currently has over 50 problems that are tagged by important problem attributes such as type of decision variables, and nature of constraints. Approximately half of the problems in the(More)
We consider simulation-optimization (SO) models where the decision variables are integer ordered and the objective function is defined implicitly via a simulation oracle, which for any feasible solution can be called to compute a point estimate of the objective-function value. We develop R-SPLINE---a Retrospective-search algorithm that alternates between a(More)