Publicação
An adaptive lexicographic MILP approach for cut order planning
| Resumo: | Cut Order Planning is one of the most important optimization problems in the apparel industry, where fabric generally accounts for more than half of overall manufacturing costs. As an NP-hard problem, Cut Order Planning is especially challenging for large-scale industrial orders, where manual or heuristic planning techniques usually lead to fabric waste and reduced cost-effectiveness. Still, existing models tend to remain detached from the actual operative realities in apparel manufacturing plants, leaving a gap this thesis aims to address. This research develops a lexicographic Mixed-Integer Linear Programming model, optimized using the Gurobi solver, to address the challenges in industrial Cut Order Planning. The mathematical model applies a hierarchical objective formulation to find balanced solutions by sequentially targeting three key goals: (i) minimization of fabric consumption, (ii) reduction of operational complexity by limiting the number of markers, and (iii) improvement order fulfilment reliability through minimization of quantity deviations. To bridge the divide between theoretical optimization and industrial practice, a lexicographic MILP optimization framework is proposed. This proposed solution constructs optimal, operationally balanced cut plans by solving the MILP problem over an existing marker database, while a complementary heuristic process identifies new sets of markers to adaptively expand that database for long-term improvement. The system was validated against industrial problem instances for performance metrics, demonstrating a measurable improvement in fabric savings and significantly greater planning reliability. This research establishes a powerful and practical decision-support tool that strengthens the state of the art in cut order planning by matching theory with industrial applicability. |
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| Autores principais: | Nunes, Leonardo Rafael Araújo |
| Assunto: | Apparel Industry Cut Order Planning Cutting and Packing Mixed-Integer Programming Optimization Indústria do Vestuário Planeamento de Ordens de Corte Corte e Embalagem Programação Inteira Mista Otimização |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Cut Order Planning is one of the most important optimization problems in the apparel industry, where fabric generally accounts for more than half of overall manufacturing costs. As an NP-hard problem, Cut Order Planning is especially challenging for large-scale industrial orders, where manual or heuristic planning techniques usually lead to fabric waste and reduced cost-effectiveness. Still, existing models tend to remain detached from the actual operative realities in apparel manufacturing plants, leaving a gap this thesis aims to address. This research develops a lexicographic Mixed-Integer Linear Programming model, optimized using the Gurobi solver, to address the challenges in industrial Cut Order Planning. The mathematical model applies a hierarchical objective formulation to find balanced solutions by sequentially targeting three key goals: (i) minimization of fabric consumption, (ii) reduction of operational complexity by limiting the number of markers, and (iii) improvement order fulfilment reliability through minimization of quantity deviations. To bridge the divide between theoretical optimization and industrial practice, a lexicographic MILP optimization framework is proposed. This proposed solution constructs optimal, operationally balanced cut plans by solving the MILP problem over an existing marker database, while a complementary heuristic process identifies new sets of markers to adaptively expand that database for long-term improvement. The system was validated against industrial problem instances for performance metrics, demonstrating a measurable improvement in fabric savings and significantly greater planning reliability. This research establishes a powerful and practical decision-support tool that strengthens the state of the art in cut order planning by matching theory with industrial applicability. |
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