Imagen de Portada

Neural-Driven Constructive Heuristic for 2D Robotic Bin Packing Problem

Guardado en:
Publicado en:Electronics vol. 14, no. 10 (2025), p. 1956
Autor principal: Kaleta Mariusz
Otros Autores: Śliwiński Tomasz
Publicado:
MDPI AG
Materias:
Covariance matrix
Heuristic
Datasets
Neural networks
Combinatorial analysis
Genetic algorithms
Optimization
Algorithms
Linear programming
Packing problem
Automation
Machine learning
Packaging
Heuristic methods
Robotics
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:This study addresses the two-dimensional weakly homogeneous Bin Packing Problem (2D-BPP) in the context of robotic packing, where items must be arranged in a manner feasible for robotic manipulation. Traditional heuristics for this NP-hard problem often lack adaptability across diverse datasets, while metaheuristics typically suffer from slow convergence. To overcome these limitations, we propose a novel neural-driven constructive heuristic. The method employs a population of simple feed-forward neural networks, which are trained using black-box optimization via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting neural network dynamically scores candidate placements within the constructive heuristic. Unlike conventional heuristics, the approach adapts to instance-specific characteristics without relying on predefined rules. Evaluated on datasets generated by 2DCPackGen and real-world logistic scenarios, the proposed method consistently outperforms benchmark heuristics such as MaxRects and Skyline, reducing the average number of bins required across various item types and demand ranges. The most significant improvements occur in complex instances, with up to 86% of 2DCPackGen cases yielding superior results. This heuristic offers a flexible and extremely fast, data-driven solution to the algorithm selection problem, demonstrating robustness and potential for broader application in combinatorial optimization while avoiding the scalability issues of reinforcement learning-based methods.
ISSN:2079-9292
DOI:10.3390/electronics14101956
Fuente:Advanced Technologies & Aerospace Database