Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization

By Chun-Hung Chen, Jianwu Lin, Enver Yücesan, S. Chick. Discrete event dynamic systems, 2000

784 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/89d5670c7fc763402f65cb6f8aac77486785cccd

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In Computational Theory and Mathematics

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Citations in the corpus

A Knowledge-Gradient Policy for Sequential Information Collection (2008, background): “A series of researchers beginning with [4] and continuing with [5], [9], [7], [8], [6] proposed and then refined a family of policies known as the optimal computing budget allocation (OCBA).”
Particle Swarm Methods (2018, methodology): “In such cases, specialized techniques such as the Optimal Computing Budget Allocation (OCBA) [16] have been used to optimally allocate the re-evaluations budget in order to provide reliable evaluation and identification of the promising particles [91].”
Budgeted Learning for Developing Personalized Treatment (2014, methodology): “Our work uses some of their algorithmic ideas, especially from [18].”
Selecting the best system when systems are revealed sequentially (2007, methodology): “Bayesian procedures such as Chen et al. (2000) and Chick and Inoue (2001a, 2001b) maximize the posterior probability of correct selection or minimize an opportunity cost given a simulation budget.”
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search (2006, positive): “Optimal schemes for the allocation of simulations in discrete stochastic optimization [13, 16, 3], could also be applied to Monte-Carlo tree search.”
Simple Bayesian Algorithms for Best Arm Identification (2016, background): “There is a quite a large literature that aims to implement optimal large deviations allocations derived in Glynn and Juneja [2004], or a simpler approximation to these in the Gaussian case known as the OCBA [Chen et al., 2000].”
The Knowledge-Gradient Policy for Correlated Normal Beliefs (2009, background): “Although OCBA- and VIP-based policies for exploiting common random numbers have also been introduced in Chick and Inoue (2001a) and Fu et al. (2007), to our knowledge, no work has been done within the R&S literature to exploit the dependence inherent in our prior belief about the values of related a”
Optimization for Simulation : Theory vs . Practice (2002, methodology): “The optimal budget computing allocation (OBCA) approach (Chen, Chen, and Yucesan 2000, Chen et al. 2000) is one link between these two, and would seem to be a candidate for commercial development due to its relative ease of imple- mentation.”