Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms

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dc.contributor.affiliationAl-Mahmud S., University of Applied Sciences and Arts, Fachhochschule Dortmund, Emil-Figge-Str. 44, Dortmund, 44227, Germany
dc.contributor.affiliationCano J.A., University of Medellin, Carrera 87 # 30-65, Medellin, 050026, Colombia
dc.contributor.affiliationCampo E.A., University of Medellin, Carrera 87 # 30-65, Medellin, 050026, Colombia
dc.contributor.affiliationWeyers S., University of Applied Sciences and Arts, Fachhochschule Dortmund, Emil-Figge-Str. 44, Dortmund, 44227, Germany
dc.contributor.authorAl-Mahmud S.
dc.contributor.authorCano J.A.
dc.contributor.authorCampo E.A.
dc.contributor.authorWeyers S.
dc.date.accessioned2025-09-08T14:23:54Z
dc.date.available2025-09-08T14:23:54Z
dc.date.issued2025
dc.descriptionCut Order Planning (COP) optimizes production costs in the apparel industry by efficiently cutting fabric for garments. This complex process involves challenging decision-making due to order specifications and production constraints. This article introduces novel approaches to the COP problem using heuristics, metaheuristic algorithms, and commercial solvers. Two different solution approaches are proposed and tested through experimentation and analysis, demonstrating their effectiveness in real-world scenarios. The first approach uses conventional metaheuristic algorithms, while the second transforms the nonlinear COP mathematical model into a Mixed Integer Linear Programming (MILP) problem and uses commercial solvers for solution. Modifications to existing heuristics, combined with tournament selection in genetic algorithms (GA), improve solution quality and efficiency. Comparative analysis shows that Particle Swarm Optimization (PSO) outperforms GA, especially for small and medium-sized problems. Cost and runtime evaluations confirm the efficiency and practical applicability of the proposed algorithms, with commercial solvers, delivering superior solutions in shorter computation times. This study suggests the use of solvers for the COP problem, especially for smaller orders, and reserves PSO and GA for larger orders where commercial solvers may not provide a solution. © 2025 Universidad Politecnica de Valencia. All rights reserved.
dc.identifier.doi10.4995/ijpme.2025.22196
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn23405317
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/9131
dc.language.isoeng
dc.publisher.facultyFacultad de Ciencias Económicas y Administrativasspa
dc.publisher.programAdministración de Empresasspa
dc.publisher.programNegocios Internacionalesspa
dc.relation.citationendpage26
dc.relation.citationissue1
dc.relation.citationstartpage1
dc.relation.citationvolume13
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-86000369822&doi=10.4995%2fijpme.2025.22196&partnerID=40&md5=fd0017e61eb26264ea9ba48ff39607ce
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dc.rights.accesoRestricted access
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceInternational Journal of Production Management and Engineering
dc.sourceInt. J. Prod. Manag. Eng.
dc.sourceScopus
dc.subjectCOP
dc.subjectCut order planning
dc.subjectGarment manufacturing
dc.subjectHeuristics
dc.subjectMetaheuristics
dc.subjectMILP
dc.titleOptimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms
dc.typeArticle
dc.type.localArtículospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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