Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms
Cut 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...
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| Format: | Article |
| Language: | English |
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Universitat Politècnica de València
2025-01-01
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| Series: | International Journal of Production Management and Engineering |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/IJPME/article/view/22196 |
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| author | Sharif Al-Mahmud Jose Alejandro Cano Emiro Antonio Campo Stephan Weyers |
| author_facet | Sharif Al-Mahmud Jose Alejandro Cano Emiro Antonio Campo Stephan Weyers |
| author_sort | Sharif Al-Mahmud |
| collection | DOAJ |
| description | Cut 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. |
| format | Article |
| id | doaj-art-a147e424104d4f4b8a058c78274ed77d |
| institution | Kabale University |
| issn | 2340-4876 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | International Journal of Production Management and Engineering |
| spelling | doaj-art-a147e424104d4f4b8a058c78274ed77d2025-01-31T13:48:47ZengUniversitat Politècnica de ValènciaInternational Journal of Production Management and Engineering2340-48762025-01-0113112610.4995/ijpme.2025.2219621386Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithmsSharif Al-Mahmud0https://orcid.org/0000-0001-7411-7349Jose Alejandro Cano1https://orcid.org/0000-0002-2638-5581Emiro Antonio Campo2https://orcid.org/0000-0003-1376-2111Stephan Weyers3Dortmund University of Applied Sciences and ArtsUniversidad de MedellínUniversidad de MedellínDortmund University of Applied Sciences and Arts Cut 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.https://polipapers.upv.es/index.php/IJPME/article/view/22196copcut order planningheuristicsmetaheuristicsmilpgarment manufacturing |
| spellingShingle | Sharif Al-Mahmud Jose Alejandro Cano Emiro Antonio Campo Stephan Weyers Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms International Journal of Production Management and Engineering cop cut order planning heuristics metaheuristics milp garment manufacturing |
| title | Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms |
| title_full | Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms |
| title_fullStr | Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms |
| title_full_unstemmed | Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms |
| title_short | Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms |
| title_sort | optimizing cut order planning a comparative study of heuristics metaheuristics and milp algorithms |
| topic | cop cut order planning heuristics metaheuristics milp garment manufacturing |
| url | https://polipapers.upv.es/index.php/IJPME/article/view/22196 |
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