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Cluster-Based Path Optimization Framework for Garment Cutting Using K-Means and CAC-LK
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DOI：10.3390/app16073420.png)
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Abstract
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Featured Application The proposed K-means + CAC-LK framework can be deployed in automated garment-cutting and textile-recycling production lines to reduce the idle travel of cutting heads, improve equipment utilization, and support real-time path planning for medium-to-large-scale nesting tasks. It can also be extended to other contour-based Computer Numerical Control (CNC) machining scenarios, such as leather cutting, fabric trimming, and sheet-material processing, where non-cutting travel significantly affects production efficiency. It can also be extended to other contour-based CNC machining scenarios, such as leather cutting, fabric trimming, and sheet-material processing, where non-cutting travel significantly affects production efficiency.Abstract In automated garment-cutting systems, idle-travel path planning becomes computationally expensive when the number of cutting pieces reaches medium-to-large scales (80-150 nodes), directly affecting production efficiency. To address the limitations of traditional heuristic methods in solution quality and runtime stability, this study proposes a cluster-based local search framework integrating K-means clustering with a Cluster-Aware Constrained Lin-Kernighan (CAC-LK) algorithm. K-means partitions entry points into compact spatial clusters to reduce the computational scale, and an adaptive depth-constrained CAC-LK procedure optimizes intra-cluster paths while maintaining a predictable runtime. Inter-cluster routes are connected using a nearest-neighbor strategy. Experiments on simulated datasets with 85 and 140 nodes show that the proposed method reduces the idle-travel distance by 4-10% compared with K-means + 3-opt while achieving a more stable runtime than unconstrained K-means + LK. The results demonstrate that the proposed framework provides an effective balance between path quality, scalability, and computational stability, showing strong applicability for real-time intelligent garment-cutting systems.
Keywords:
garment-cutting path optimization
K-means clustering
CAC-LK algorithm
local search
cluster-based path planning
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