How to optimize discrete-event systems from a single sample path by the score function method
Annals of Operations Research
WSC '96 Proceedings of the 28th conference on Winter simulation
Discrete stochastic optimization using linear interpolation
Proceedings of the 40th Conference on Winter Simulation
Use of retrospective optimization for placement of oil wells under uncertainty
Proceedings of the Winter Simulation Conference
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We consider optimizing a stochastic system, given only a simulation model that is parameterized by continuous decision variables. The model is assumed to produce unbiased point estimates of the system performance measure(s), which must be expected values. The performance measures may appear in the objective function and/or in the constraints. We develop a family of retrospective-optimization (RO) algorithms based on a sequence of sample-path approximations to the original problem with increasing sample sizes. Each approximation problem is obtained by substituting point estimators for each performance measure and using common random numbers over all values of the decision variables. We assume that these approximation problems can be deterministically solved to within a specified error in the decision variables, and that this error is decreasing to zero. The computational efficiency of RO arises from being able to solve the next approximation problem efficiently based on knowledge gained from the earlier, easier approximation problems.