An artificial neural network meta-model for constrained simulation optimization

Ali Mohammad Nezhad and Hashem Mahlooji
Additional contact information
Ali Mohammad Nezhad: Sharif University of Technology, Tehran, Iran
Hashem Mahlooji: Sharif University of Technology, Tehran, Iran

Journal of the Operational Research Society, 2014, vol. 65, issue 8, 1232-1244

Abstract: This paper presents artificial neural network (ANN) meta-models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta-models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non-linear nature of the ANN, the SO problem is iteratively approximated via non-linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane-relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition is met. To study the robustness and efficiency of the proposed algorithm, a realistic inventory model is used; the results are compared with those of the OptQuest optimization package. These numerical results indicate that the proposed meta-model-based algorithm performs quite competitively while requiring slightly fewer simulation observations.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.palgrave-journals.com/jors/journal/v65/n8/pdf/jors201373a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/jors/journal/v65/n8/full/jors201373a.html Link to full text HTML (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:65:y:2014:i:8:p:1232-1244

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().