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Comparison of two problem transformation-based methods in detecting the best performing branch-and-bound procedures for the RCPSP

2025-07-01Artigo científico Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorGuo, Weikang
dc.contributor.authorCoelho, José
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2026-01-17T09:12:55Z
dc.date.available2026-01-17T09:12:55Z
dc.date.issued2025-07-01
dc.description.abstractThe branch-and-bound (B&B) procedure is one of the most frequently used methods for solving the resource constrained project scheduling problem (RCPSP) to obtain optimal solutions and has a rich history in the academic literature. Over the past decades, various variants of this procedure have been proposed, each using slightly different configurations to search for the optimal solution. While most of the configurations perform relatively well for many problem instances, there is, however, no known universal best B&B configuration that works well for all problem instances. In this work, we propose two problem transformation-based machine learning classification methods (binary relevance and classifier chains) to automatically detect the best-performing branch-and-bound configuration for the resource-constrained project scheduling problem. The proposed novel learning models aim to find the relationship between the project characteristics and the performance of a specific B&B configuration. With this obtained knowledge, the best-performing B&B configurations can be predicted, resulting in a better solution. A comprehensive computational experiment is conducted to demonstrate the effectiveness of the proposed classification models and the performance improvements over three categories of methods from the literature, including the latest branch-and-bound configurations, the state-of-the-art classification models in project scheduling, and commonly used clustering algorithms in machine learning. The results show that the proposed classification models can enhance solution quality for the RCPSP without changing the core components of existing algorithms. More specifically, the classifier chains method, when combined with the BackPropagation Neural Network algorithm, achieves the best performance, outperforming binary relevance, which demonstrates the impact of label correlation on the performance. The experiments also demonstrate the merits of the proposed model in improving the robustness of the solutions. Furthermore, these findings not only highlight the potential of the classification models in detecting best-performing B&B configurations, but also emphasize the need for future work and development to further improve the performance and applicability of these modelseng
dc.identifier.citationGuo, W., Vanhoucke, M., & Coelho, J. (2025). Comparison of two problem transformation-based methods in detecting the best performing branch-and-bound procedures for the RCPSP. Expert Systems with Applications, 281, 127383. https://doi.org/10.1016/j.eswa.2025.127383
dc.identifier.doi10.1016/j.eswa.2025.127383
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/10400.2/20943
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S095741742501005X
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectProject scheduling
dc.subjectMachine learning
dc.subjectProblem transformation
dc.subjectClassification
dc.subjectPerformance detection
dc.titleComparison of two problem transformation-based methods in detecting the best performing branch-and-bound procedures for the RCPSPeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage22
oaire.citation.issue127383
oaire.citation.startPage1
oaire.citation.titleExpert Systems with Applications
oaire.citation.volume281
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCoelho
person.familyNameVanhoucke
person.givenNameJosé
person.givenNameMario
person.identifierR-000-8V7
person.identifier.ciencia-id7D18-9842-159F
person.identifier.orcid0000-0002-5855-284X
person.identifier.orcid0000-0001-6702-3563
person.identifier.ridD-8647-2015
person.identifier.scopus-author-id6507772652
relation.isAuthorOfPublication2926ed15-fe04-4ee4-a40d-ad0a83e33af8
relation.isAuthorOfPublication129fc49c-d742-406a-b680-f5544f8da0e2
relation.isAuthorOfPublication.latestForDiscovery2926ed15-fe04-4ee4-a40d-ad0a83e33af8

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