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Cutting and packing problems are critical in industrial applications where efficient space utilization directly impacts cost
reduction and resource optimization. These problems arise in manufacturing, logistics, and material usage planning, where the goal is to
arrange objects of varying dimensions within a finite space while minimizing unused areas and reducing waste. Traditional solutions, including
robust algorithms, often struggle with high computational complexity when scaling to larger problem instances.
This study introduces a Genetic Algorithm (GA)-based approach to address the cutting and packing problem by leveraging evolutionary
optimization techniques. The proposed method represents object placement sequences and orientations as chromosomes, applying selection,
crossover, and mutation operators to iteratively refine solutions. A specialized fitness function is designed to maximize material utilization
while ensuring feasible arrangements.
The experimental evaluation involved multiple test cases with varying object sizes and space constraints. Results indicate that the GA-based
approach achieves a 30-50% reduction in material waste compared to random placement strategies. Furthermore, a comparative analysis
against traditional greedy methods demonstrates that GA provides superior adaptability and efficiency, particularly in non-trivial configurations
where local optimizations fail to generalize effectively.
The findings of this study highlight the potential of genetic algorithms in solving complex cutting and packing problems, offering a robust
balance between computational feasibility and solution quality.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Bahaeddin Turkoglu