Feature Article: Optimization for simulation: Theory vs. Practice

By Michael C. Fu. INFORMS journal on computing, 2002

630 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3dc74613b4184339824bc883a4e03b054f3faf98

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Citations in the corpus

Simulation optimization: a review, new developments, and applications (2005, methodology): “Comparing with Table 1 in Fu (2002), one observes that ProModel and SIMUL8 have both migrated to OptQuest from their previous simulation optimization packages (SimRunner and OPTIMIZ, respectively).”
A simulation based optimization approach to supply chain management under demand uncertainty (2004, background): “Fu (2002) and Fu and Hu (1997) address the theoretical aspect of gradient based approaches to simulation optimization which will be applied in our work.”
The Knowledge-Gradient Policy for Correlated Normal Beliefs (2009, methodology): “The R&S and experimental design literature has devoted the most attention to our problem class (see Bechhofer et al. 1995 for a comprehensive treatment of R&S, and Fu 2002 and Swisher et al. 2003 for a review of R&S within the simulation community).”
Reinforcement learning of motor skills with policy gradients (2008, background): “This simplification is well known to introduce similar exploration over all degrees of freedom and has been shown to reduce the variance in the gradient estimate (Fu, 2002).”
A unified framework for stochastic optimization (2019, unknown): “Other reviews inlude ( Andradóttir, 1998a; 1998b; Azadivar, 1999 ), Fu (2002) , and im and Nelson (2007) .”
Selecting the best system when systems are revealed sequentially (2007, methodology): “Another instance is optimization via simulation (see Fu (2002) for a thorough review).”
A survey on metaheuristics for stochastic combinatorial optimization (2009, background): “Interestingly, as noted by Fu (2002), the many new commercial software packages for simulation do not take advantage of the theoretical results of the literature.”