Sustainable optimisation approaches for production planning and control to evolve towards industry 5.0.
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| Title: | Sustainable optimisation approaches for production planning and control to evolve towards industry 5.0. |
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| Authors: | Guerrero, Blanca1 (AUTHOR), Mula, Josefa1 (AUTHOR) fmula@cigip.upv.es, Poler, Raul1 (AUTHOR) |
| Source: | International Journal of Production Research. Nov2025, Vol. 63 Issue 21, p8091-8123. 33p. |
| Subjects: | Industry 4.0, Artificial intelligence, Digital transformation, Mathematical programming, Ergonomics, Production planning, Data analytics, Economic efficiency |
| Abstract: | Industry 4.0 (I4.0) has led to a very high development potential in the optimisation of production planning and control problems in industrial companies. This study aims to analyse the existing scientific literature on the optimisation of planning, control and management problems, specifically in the production, operations and scheduling areas, to make industry more sustainable in accordance with the I4.0 context; that is, applying new digital technologies and I5.0 by integrating people into this digital transformation. Specifically, 77 research works were identified and analysed. The main findings conclude that the key setting of these optimisation problems lies in manufacturing contexts and highlight the use of modelling and solution techniques through mathematical programming and metaheuristics. These studies, which are mostly empirical, point out different software tools, and underline the use of MATLAB, optimisation with CPLEX, the Python programming language and simulation through AnyLogic. For I4.0 techniques, data analytics and big data, artificial intelligence, the Internet of Things, sensors and simulation and digital twins are the most studied. Proposals addressing worker ergonomics and a three-pillar approach to sustainability are particularly noteworthy. Finally, the main research gaps, challenges and trends for future research are recognised. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 189009322 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sustainable optimisation approaches for production planning and control to evolve towards industry 5.0. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guerrero%2C+Blanca%22">Guerrero, Blanca</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mula%2C+Josefa%22">Mula, Josefa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fmula@cigip.upv.es</i><br /><searchLink fieldCode="AR" term="%22Poler%2C+Raul%22">Poler, Raul</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Nov2025, Vol. 63 Issue 21, p8091-8123. 33p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+transformation%22">Digital transformation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Ergonomics%22">Ergonomics</searchLink><br /><searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+efficiency%22">Economic efficiency</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Industry 4.0 (I4.0) has led to a very high development potential in the optimisation of production planning and control problems in industrial companies. This study aims to analyse the existing scientific literature on the optimisation of planning, control and management problems, specifically in the production, operations and scheduling areas, to make industry more sustainable in accordance with the I4.0 context; that is, applying new digital technologies and I5.0 by integrating people into this digital transformation. Specifically, 77 research works were identified and analysed. The main findings conclude that the key setting of these optimisation problems lies in manufacturing contexts and highlight the use of modelling and solution techniques through mathematical programming and metaheuristics. These studies, which are mostly empirical, point out different software tools, and underline the use of MATLAB, optimisation with CPLEX, the Python programming language and simulation through AnyLogic. For I4.0 techniques, data analytics and big data, artificial intelligence, the Internet of Things, sensors and simulation and digital twins are the most studied. Proposals addressing worker ergonomics and a three-pillar approach to sustainability are particularly noteworthy. Finally, the main research gaps, challenges and trends for future research are recognised. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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: 10.1080/00207543.2025.2507794 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 8091 Subjects: – SubjectFull: Industry 4.0 Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Digital transformation Type: general – SubjectFull: Mathematical programming Type: general – SubjectFull: Ergonomics Type: general – SubjectFull: Production planning Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Economic efficiency Type: general Titles: – TitleFull: Sustainable optimisation approaches for production planning and control to evolve towards industry 5.0. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guerrero, Blanca – PersonEntity: Name: NameFull: Mula, Josefa – PersonEntity: Name: NameFull: Poler, Raul IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 63 – Type: issue Value: 21 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
