Abstract
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.
Schlagwörter
COP
MILP
Metaheuristics
cut order planning
garment manufacturing
heuristics