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Book Chapter: Surrogate-Based Simulation Optimization
Title | Surrogate-Based Simulation Optimization |
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Authors | |
Keywords | surrogate simulation optimization Gaussian process matrix inversion |
Issue Date | 2021 |
Publisher | INFORMS |
Citation | Surrogate-Based Simulation Optimization. In Carlsson, JG (Ed.), Emerging Optimization Methods and Modeling Techniques with Applications, p. 287-311. Catonsville, MD: INFORMS, 2021 How to Cite? |
Abstract | Simulation models are widely used in practice to facilitate decision making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and optimize because of a lack of analytical tractability. Simulation optimization is concerned with developing efficient sampling schemes—subject to computational budgets—to solve such optimization problems. To mitigate the computational burden, surrogates are often constructed using simulation outputs to approximate the response surface of the simulation model. In this tutorial, we provide an up-to-date overview of surrogate-based methods for simulation optimization with continuous decision variables. Typical surrogates, including linear basis function models and Gaussian processes, are introduced. Surrogates can be used as either local approximations or global approximations. Depending on the choice, one may develop algorithms that converge to either a local optimum or a global optimum. Representative examples are presented for each category. Recent advances in large-scale computation for Gaussian processes are also discussed. |
Description | Presented at the TutORials Session, INFORMS Annual Meeting, Virtual Meeting, Anaheim, CA, USA, October 24–27, 2021 |
Persistent Identifier | http://hdl.handle.net/10722/306265 |
ISBN | |
Series/Report no. | INFORMS TutORials in Operations Research |
DC Field | Value | Language |
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dc.contributor.author | Hong, LJ | - |
dc.contributor.author | Zhang, X | - |
dc.date.accessioned | 2021-10-20T10:21:09Z | - |
dc.date.available | 2021-10-20T10:21:09Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Surrogate-Based Simulation Optimization. In Carlsson, JG (Ed.), Emerging Optimization Methods and Modeling Techniques with Applications, p. 287-311. Catonsville, MD: INFORMS, 2021 | - |
dc.identifier.isbn | 9780990615354 | - |
dc.identifier.uri | http://hdl.handle.net/10722/306265 | - |
dc.description | Presented at the TutORials Session, INFORMS Annual Meeting, Virtual Meeting, Anaheim, CA, USA, October 24–27, 2021 | - |
dc.description.abstract | Simulation models are widely used in practice to facilitate decision making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and optimize because of a lack of analytical tractability. Simulation optimization is concerned with developing efficient sampling schemes—subject to computational budgets—to solve such optimization problems. To mitigate the computational burden, surrogates are often constructed using simulation outputs to approximate the response surface of the simulation model. In this tutorial, we provide an up-to-date overview of surrogate-based methods for simulation optimization with continuous decision variables. Typical surrogates, including linear basis function models and Gaussian processes, are introduced. Surrogates can be used as either local approximations or global approximations. Depending on the choice, one may develop algorithms that converge to either a local optimum or a global optimum. Representative examples are presented for each category. Recent advances in large-scale computation for Gaussian processes are also discussed. | - |
dc.language | eng | - |
dc.publisher | INFORMS | - |
dc.relation.ispartof | Emerging Optimization Methods and Modeling Techniques with Applications | - |
dc.relation.ispartofseries | INFORMS TutORials in Operations Research | - |
dc.subject | surrogate | - |
dc.subject | simulation optimization | - |
dc.subject | Gaussian process | - |
dc.subject | matrix inversion | - |
dc.title | Surrogate-Based Simulation Optimization | - |
dc.type | Book_Chapter | - |
dc.identifier.email | Zhang, X: xiaoweiz@hku.hk | - |
dc.identifier.authority | Zhang, X=rp02554 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1287/educ.2021.0225 | - |
dc.identifier.hkuros | 327194 | - |
dc.identifier.spage | 287 | - |
dc.identifier.epage | 311 | - |
dc.publisher.place | Catonsville, MD | - |