Mathematical programming models to tackle the COVID-19 pandemic: operations research perspective.
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| Title: | Mathematical programming models to tackle the COVID-19 pandemic: operations research perspective. |
|---|---|
| Authors: | Yesilkaya, Murat1 (AUTHOR) murat.yesilkaya@gop.edu.tr, Tirkolaee, Erfan Babaee2,3 (AUTHOR) |
| Source: | RAIRO: Operations Research (2804-7303). Mar/Apr2026, Vol. 60 Issue 2, p907-953. 47p. |
| Subjects: | COVID-19 pandemic, Mathematical programming, Supply chain management, Health care industry, Operations research, Mathematical optimization, Vehicle routing problem, Scheduling |
| Abstract: | The novel coronavirus pandemic (COVID-19) massively disrupted daily life globally and locally, resulting in many issues. Since the pandemic started, Operations Research (OR) scholars have conducted research and published studies on various issues raised by COVID-19. They developed various mathematical programming models (MPMs) to optimize and plan for the problems caused by the pandemic. This work aims to provide researchers with a starting point for MPMs in the management and planning of the COVID-19 pandemic. First, a bibliometric analysis of the previous studies is performed, wherein the models are proposed to tackle the COVID-19 pandemic. Then, it systematically reviewed the deterministic MPMs used in the fight against COVID-19 and analyzed the current trends in the literature, the types of models used, OR application areas, objective functions, constraint structures, and solution methods. These studies are classified in the OR literature into the following approaches: linear programming (LP), mixed-integer linear programming (MILP), multi-objective programming (MOP), goal programming (GP), and bi-level programming (BLP). The findings show that MPMs provide significant benefits in tackling the pandemic in various areas such as healthcare systems, supply chain management, vehicle routing, scheduling, and assignment. Moreover, the research challenges, trends, and outlook for future studies are discussed, which will guide researchers and practitioners to conduct more applicable and efficient research and determine optimal policies, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193984841 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mathematical programming models to tackle the COVID-19 pandemic: operations research perspective. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yesilkaya%2C+Murat%22">Yesilkaya, Murat</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> murat.yesilkaya@gop.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Tirkolaee%2C+Erfan+Babaee%22">Tirkolaee, Erfan Babaee</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22RAIRO%3A+Operations+Research+%282804-7303%29%22">RAIRO: Operations Research (2804-7303)</searchLink>. Mar/Apr2026, Vol. 60 Issue 2, p907-953. 47p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br /><searchLink fieldCode="DE" term="%22Health+care+industry%22">Health care industry</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+research%22">Operations research</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Vehicle+routing+problem%22">Vehicle routing problem</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The novel coronavirus pandemic (COVID-19) massively disrupted daily life globally and locally, resulting in many issues. Since the pandemic started, Operations Research (OR) scholars have conducted research and published studies on various issues raised by COVID-19. They developed various mathematical programming models (MPMs) to optimize and plan for the problems caused by the pandemic. This work aims to provide researchers with a starting point for MPMs in the management and planning of the COVID-19 pandemic. First, a bibliometric analysis of the previous studies is performed, wherein the models are proposed to tackle the COVID-19 pandemic. Then, it systematically reviewed the deterministic MPMs used in the fight against COVID-19 and analyzed the current trends in the literature, the types of models used, OR application areas, objective functions, constraint structures, and solution methods. These studies are classified in the OR literature into the following approaches: linear programming (LP), mixed-integer linear programming (MILP), multi-objective programming (MOP), goal programming (GP), and bi-level programming (BLP). The findings show that MPMs provide significant benefits in tackling the pandemic in various areas such as healthcare systems, supply chain management, vehicle routing, scheduling, and assignment. Moreover, the research challenges, trends, and outlook for future studies are discussed, which will guide researchers and practitioners to conduct more applicable and efficient research and determine optimal policies, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1051/ro/2026018 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 47 StartPage: 907 Subjects: – SubjectFull: COVID-19 pandemic Type: general – SubjectFull: Mathematical programming Type: general – SubjectFull: Supply chain management Type: general – SubjectFull: Health care industry Type: general – SubjectFull: Operations research Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Vehicle routing problem Type: general – SubjectFull: Scheduling Type: general Titles: – TitleFull: Mathematical programming models to tackle the COVID-19 pandemic: operations research perspective. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yesilkaya, Murat – PersonEntity: Name: NameFull: Tirkolaee, Erfan Babaee IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar/Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 28047303 Numbering: – Type: volume Value: 60 – Type: issue Value: 2 Titles: – TitleFull: RAIRO: Operations Research (2804-7303) Type: main |
| ResultId | 1 |
