Fifty years of stochastic simulation: Where we are and where we need to go.
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| Title: | Fifty years of stochastic simulation: Where we are and where we need to go. |
|---|---|
| Authors: | Hong, L. Jeff1 (AUTHOR) lhong@umn.edu, Nelson, Barry L.2 (AUTHOR) |
| Source: | European Journal of Operational Research. May2026, Vol. 330 Issue 3, p701-714. 14p. |
| Subjects: | Simulation methods & models, Operations research, Dynamical systems, Monte Carlo method, Uncertainty (Information theory) |
| Abstract: | Stochastic computer simulation is the go-to tool for operational researchers designing and improving complex systems that must perform in the face of uncertainty. In this article, we reflect on key advances in simulation analysis methodology over the past 50 years and speculate on future research directions, employing three recent real applications of simulation to ground our discussion. • Reflect on key advances in simulation analysis methodology over the past 50 years. • Speculate on future research directions of stochastic simulation. • Employ three recent real applications of simulation to ground our discussion. [ABSTRACT FROM AUTHOR] |
| Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191350955 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fifty years of stochastic simulation: Where we are and where we need to go. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hong%2C+L%2E+Jeff%22">Hong, L. Jeff</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lhong@umn.edu</i><br /><searchLink fieldCode="AR" term="%22Nelson%2C+Barry+L%2E%22">Nelson, Barry L.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. May2026, Vol. 330 Issue 3, p701-714. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+research%22">Operations research</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Stochastic computer simulation is the go-to tool for operational researchers designing and improving complex systems that must perform in the face of uncertainty. In this article, we reflect on key advances in simulation analysis methodology over the past 50 years and speculate on future research directions, employing three recent real applications of simulation to ground our discussion. • Reflect on key advances in simulation analysis methodology over the past 50 years. • Speculate on future research directions of stochastic simulation. • Employ three recent real applications of simulation to ground our discussion. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191350955 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ejor.2025.06.033 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 701 Subjects: – SubjectFull: Simulation methods & models Type: general – SubjectFull: Operations research Type: general – SubjectFull: Dynamical systems Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Uncertainty (Information theory) Type: general Titles: – TitleFull: Fifty years of stochastic simulation: Where we are and where we need to go. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hong, L. Jeff – PersonEntity: Name: NameFull: Nelson, Barry L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03772217 Numbering: – Type: volume Value: 330 – Type: issue Value: 3 Titles: – TitleFull: European Journal of Operational Research Type: main |
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