Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

Background: The Irregular Strip Packing Problem (ISPP) involves packing a set of irregularly shaped items within a strip while minimizing its length. Methods: This study introduces the Normalized Direct Trigonometry Model (NDTM), an innovative enhancement of the Direct Trigonometry Model (DTM). The NDTM incorporates a distance function that supports the integration of the separation constraint, which mandates a minimum separation distance between items. Additionally, the paper proposes a new set of constraints based on the bounding boxes of the pieces aimed at improving the non-overlapping condition. Results: Comparative computational experiments were performed using a comprehensive set of 90 instances. Results show that the NDTM finds more feasible and optimal solutions than the DTM. While the NDTM allows for the implementation of the separation constraint, the number of feasible and optimal solutions tends to decrease as more separation among the items is considered, despite not increasing the number of variables or constraints. Conclusions: The NDTM outperforms the DTM. Moreover, the results indicate that the new set of non-overlapping constraints facilitates the exploration of feasible solutions at the expense of optimality in some cases.

The rapid growth of the e-commerce sector, particularly in Latin America, has highlighted the need for more efficient automated packing and distribution systems. This study presents heuristic algorithms to solve the online three-dimensional knapsack problem (OSKP), incorporating buffering and repacking strategies to optimize space utilization in automated packing environments. These strategies enable the system to handle the stochastic nature of item arrivals and improve container utilization by temporarily storing boxes (buffering) and rearranging already packed boxes (repacking) to enhance packing efficiency. Computational experiments conducted on specialized datasets from the existing literature demonstrate that the proposed heuristics perform comparably to state-of-the-art methodologies. Moreover, physical experiments were conducted on a robotic packing cell to determine the time that buffering and repacking implicate. The contributions of this paper lie in the integration of buffering and repacking into the OSKP, the development of tailored heuristics, and the validation of these heuristics in both simulated and real-world environments. The findings indicate that including buffering and repacking strategies significantly improves space utilization in automated packing systems. However, they significantly increase the time spent packing.

Convex decomposition plays a central role in computational geometry and is a key preprocessing step in applications such as robotic motion planning, 2D packing, pattern recognition, and manufacturing. This work revisits the minimum convex decomposition problem and proposes both an exact mathematical model and an efficient heuristic algorithm capable of handling simple polygons as well as polygons with holes. The methodology incorporates a visibility-preserving bridge transformation that converts holed polygons into equivalent simple instances, enabling the extension of classical decomposition schemes to more general topologies. In addition, a convex-union post-processing phase is implemented to reduce the number of convex parts obtained by either method. The performance of the proposed approach is evaluated on benchmark instances from the literature and on a new dataset of polygons with holes introduced in this work. The exact model consistently produces optimal decompositions for small and medium instances, while the heuristic achieves near-optimal solutions with significantly reduced computation times. The union phase further decreases the number of resulting convex pieces in most cases. All codes, datasets, and results are publicly released to facilitate reproducibility and comparison with future methods.

La descomposición de una figura poligonal en un conjunto de polígonos convexos es útil en el empaquetamiento de formas irregulares y el corte de materiales. Los polígonos convexos permiten que cualquier línea entre dos puntos dentro del polígono permanezca en su interior, resaltando que la figura no es hueca. En este trabajo, proponemos un algoritmo exacto y uno aproximado mejorados para la mínima descomposición convexa de polígonos. Adaptamos un modelo exacto existente, logrando soluciones óptimas, y desarrollamos un algoritmo heurístico más rápido, aunque no siempre óptimo. También implementamos un algoritmo de mejora que une subpolígonos convexos para reducir su número. Comparamos nuestros algoritmos con otros y encontramos que la heurística combinada con la unión de convexos tuvo un desempeño superior. La solución exacta es recomendada cuando el tiempo computacional no es una limitación. Para trabajos futuros, se sugiere integrar estos algoritmos en enfoques de corte bidimensional de piezas irregulares.