journal article·2024-12-31Open access

Nesting Process Automation in the Footwear Industry: A Hybrid Approach to Minimize Material Waste

Eliseo Aguilar-Tortosa, Eduard-Andrei Duta-Costache, Elías Vera-Brazal, José-Luis Sánchez-Romero, José Francisco Gómez-Hernández, Antonio Jimeno-Morenilla, Antonio Maciá-Lillo

Applied Sciences· Volume 15· Issue 1

Abstract

In any industry, maximizing the use of raw materials is essential to reduce waste and costs, which also positively impacts the environment. In footwear production, components are typically derived from cutting processes, requiring optimized systems to maximize the use of different materials, minimize waste, and accelerate production. In this context, nesting is a technique that arranges shapes within a confined space to maximize area utilization and reduce unused space. As this problem is classified as NP-Hard, only algorithmic approximations can be employed. This paper focuses on optimizing the cutting of leather parts for shoe manufacturing. Footwear parts are cut from cattle hides, which are not only irregular in shape but also vary in resistance and quality across different areas of the same piece of leather. This study proposes automated nesting methods that aim to compete with current manual approaches, which are conducted exclusively by experts with deep knowledge of the characteristics of both the pieces and the leather, making the manual process time-intensive. This research reviews current methods and introduces hybrid ones, achieving up to 38.4× acceleration and up to 10.18% increase in nested pieces over manual methods.
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Research context

OpenAlex topics: Optimization and Packing Problems · Image Processing and 3D Reconstruction · Manufacturing Process and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

INESCOP—Spanish Footwear Technology Centre, 03600 Alicante, Spain

Department of Computer Technology, University of Alicante, 03690 Alicante, Spain

Funding

Valencian Institute of Business Competitiveness (IVACE+i)

References (45)

Show source-provided references
1. Optimal Packing and Covering in the Plane Are NP-Complete10.1016/0020-0190(81)90111-3 ↗
2. Orthogonal Packings in Two Dimensions10.1137/0209064 ↗
3. Jostling for Position: Local Improvement for Irregular Cutting Patterns10.1057/palgrave.jors.2600563 ↗
4. Richard Carl, J. (1966). An Approach to the Two Dimensional Irregular Cutting Stock Problem. [Ph.D. Thesis, Massachusetts Institute of Technology].
5. Guo, B., Zhang, Y., Hu, J., Li, J., Wu, F., Peng, Q., and Zhang, Q. (2022). Two-Dimensional Irregular Packing Problems: A Review. Front. Mech. Eng., 8.10.3389/fmech.2022.966691 ↗
6. Optimal Allocation of Two-Dimensional Irregular Shapes Using Heuristic Search Methods10.1109/TSMC.1980.4308483 ↗
7. Adamowicz, M. (2024, November 30). A Two-Stage Solution of the Cutting-Stock Problem. Available online: https://www.semanticscholar.org/paper/A-Two-stage-Solution-of-the-Cutting-stock-Problem-Adamowicz-Albano/4d5e988aa8cd70bab64b2a90da3930a4474fbee1.
8. A Solution of the Rectangular Cutting-Stock Problem10.1109/TSMC.1976.5408780 ↗
9. Solving Irregular Strip Packing Problems by Hybridising Simulated Annealing and Linear Programming10.1016/j.ejor.2004.09.008 ↗
10. Integer Linear Programming Models for 2-Staged Two-Dimensional Knapsack Problems10.1007/s10107-002-0319-9 ↗
11. Approximate and Exact Algorithms for Constrained (Un) Weighted Two-Dimensional Two-Staged Cutting Stock Problems10.1023/A:1011628809603 ↗
12. TOPOS—A New Constructive Algorithm for Nesting Problems10.1007/s002910050105 ↗
13. Exact Algorithms for the 2-Dimensional Strip Packing Problem with and without Rotations10.1016/j.ejor.2008.08.020 ↗
14. Heuristic and Genetic Approach for Nesting of Two-Dimensional Rectangular Shaped Parts with Common Cutting Edge Concept for Laser Cutting and Profile Blanking Processes10.1016/j.cie.2014.11.018 ↗
15. Martinez, G., Duta, E.-A., Sanchez-Romero, J.-L., Jimeno-Morenilla, A., and Mora, H. (2024). An Efficient Algorithm for Irregular Pattern Nesting Based on Genetic Algorithm and Greedy Strategy. J. Intell. Fuzzy Syst., 1–15.10.3233/JIFS-219345 ↗
16. Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Australia.
17. An Adapted Particle Swarm Optimization Approach for a 2D Guillotine Cutting Stock Problem10.1051/meca/2015096 ↗
18. Eberhart, R., and Kennedy, J. (1995, January 4–6). A New Optimizer Using Particle Swarm Theory. Proceedings of the MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan.
19. Semis-greedy heuristics: An empirical study10.1016/0167-6377(87)90021-6 ↗
20. A Two-Stage Intelligent Search Algorithm for the Two-Dimensional Strip Packing Problem10.1016/j.ejor.2011.06.002 ↗
21. A Hybrid Heuristic Algorithm for the 2D Variable-Sized Bin Packing Problem10.1016/j.ejor.2014.03.049 ↗
22. Revolutionizing Two-Dimensional Pattern Arrangement: Innovative Image Processing Approaches10.1109/ACCESS.2024.3447092 ↗
23. Smart Nesting: Estimating Geometrical Compatibility in the Nesting Problem Using Graph Neural Networks10.1007/s10845-023-02179-0 ↗
24. Planning Irregular Object Packing via Hierarchical Reinforcement Learning10.1109/LRA.2022.3222996 ↗
25. Fang, J., Rao, Y., Zhao, X., and Du, B. (2023). A Hybrid Reinforcement Learning Algorithm for 2D Irregular Packing Problems. Mathematics, 11.10.3390/math11020327 ↗
26. A Reinforcement Learning Method for Rearranging Scattered Irregular Objects inside a Crate10.1109/TCDS.2022.3209680 ↗
27. Towards Reliable Robot Packing System Based on Deep Reinforcement Learning10.1016/j.aei.2023.102028 ↗
28. Nestability: A Deep Learning Oracle for Nesting Scrap Prediction in Manufacturing Industry10.1016/j.resconrec.2024.107540 ↗
29. Applying Machine Learning for the Anticipation of Complex Nesting Solutions in Hierarchical Production Planning10.1016/j.ejor.2021.04.006 ↗
30. Tan, K.C., Li, Y., Murray-Smith, D.J., and Sharman, K.C. (1995, January 12–14). System Identification and Linearisation using Genetic Algorithms with Simulated Annealing. Proceedings of the First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK.10.1049/cp:19951043 ↗
31. A Heuristic for Nesting Problems of Irregular Shapes10.1016/j.cad.2008.02.008 ↗
32. Zhang, Y., and Yang, C. (2009, January 14–16). A Generic Approach for Leather Nesting. Proceedings of the 2009 Fifth International Conference on Natural Computation, Tianjin, China.10.1109/ICNC.2009.45 ↗
33. The Nesting Problem in the Leather Manufacturing Industry10.1007/BF02099695 ↗
34. Genetic Algorithm Coding Methods for Leather Nesting10.1007/s10489-005-2368-2 ↗
35. Algorithms for Nesting with Defects10.1016/j.dam.2012.03.026 ↗
36. Benders Decomposition, Lagrangean Relaxation and Metaheuristic Design10.1007/s10732-007-9064-9 ↗
37. Rodríguez, A., Cuevas, F., and Esparza, D. (2023). Repair Method and Muéganos Structure Applied to the Nesting Problem in Finite Materials. Appl. Sci., 13.10.3390/app131810117 ↗
38. Rodríguez Rodríguez, A. (2023). Aplicación de Metaheurísticas en El Problema de Acomodo de Patrones de Corte en Materiales Finitos. [Ph.D. Thesis, Centro de Investigaciones en óptica, A.C.].
39. Rodríguez Rodríguez, A. (2019). Algoritmos de Visión e Inteligencia Computacional Para El Acomodo de Patrones de Corte en Materiales. [Master’s Thesis, Centro de Investigaciones en óptica, A.C.].
40. An Exhaustive Review of the Metaheuristic Algorithms for Search and Optimization: Taxonomy, Applications, and Open Challenges10.1007/s10462-023-10470-y ↗
41. A Comprehensive Review on Jaya Optimization Algorithm10.1007/s10462-022-10234-0 ↗
42. Enhanced Jaya Algorithm: A Simple but Efficient Optimization Method for Constrained Engineering Design Problems10.1016/j.knosys.2021.107555 ↗
43. Jaya: A Simple and New Optimization Algorithm for Solving Constrained and Unconstrained Optimization Problems
44. Calles-Esteban, F., Olmedo, A.A., Hellín, C.J., Valledor, A., Gómez, J., and Tayebi, A. (2024). Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach. Sensors, 24.10.3390/s24072165 ↗
45. Duzel, O., Saoud, B., Shayea, I., and Tulepova, G.N. (2024, January 27–28). Antenna Optimization Based on Genetic Algorithm. Proceedings of the 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), Gwalior, India.10.1109/AIC61668.2024.10730897 ↗