Journals / Textile and Apparel / 2026 / Volume: 36 Issue: 3

A Two-Dimensional Fabric Cutting Approach: Balancing Remnants and Overproduction

Esra Ekinci(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0003-2609-7763
Deniz Türsel Eliiyi(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0001-7693-3980
Pages
—
Publication Date
2026-10-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

A case study validates the effectiveness of the model in reducing overall waste while addressing key constraints in textile manufacturing.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The cutting stock problem (CSP) is a critical optimization problem in the textile industry, where minimizing fabric waste and overproduction is essential for cost-effective production.
📊
Key Findings & Data:The multi-objective model is solved using both preemptive and weighted goal programming approaches, offering flexibility for aligning with varying order management strategies.
💡
Conclusion & Impact:A case study validates the effectiveness of the model in reducing overall waste while addressing key constraints in textile manufacturing.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Two-dimensional cutting problem · Multi-objective Optimization · Goal Programming

Abstract

The cutting stock problem (CSP) is a critical optimization problem in the textile industry, where minimizing fabric waste and overproduction is essential for cost-effective production. This study proposes a methodology integrating a pattern generation algorithm and a multi-objective goal programming model for pattern selection. The proposed pattern generation algorithm creates guillotine cuts using a three-stage procedure, ensuring ease of applicability in manual production processes. The pattern selection model considers both fabric remnants and overproduction as waste, offering a comprehensive framework for the textile manufacturers. The multi-objective model is solved using both preemptive and weighted goal programming approaches, offering flexibility for aligning with varying order management strategies. A case study validates the effectiveness of the model in reducing overall waste while addressing key constraints in textile manufacturing. Compared to existing methods, the proposed approach enhances practicality and adaptability making it suitable for real-world textile production environments, particularly where manual cutting is employed.

🔖

Cite This Article

Ekinci, E., Eliiyi, D. T. (2026). A Two-Dimensional Fabric Cutting Approach: Balancing Remnants and Overproduction. Textile and Apparel, Volume: 36 Issue: 3. https://doi.org/10.32710/tekstilvekonfeksiyon.1603684

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (18)

  1. Martello, S., Toth, P. 1990. Knapsack Problems-Algorithms and Computer Implementations. Chichester: John Wiley & Sons.
  2. Rodrigo, W.N.P., Daundasekera, W.B., Perera, A.A.I. 2012. Pattern generation for two dimensional cutting stock problem. International Journal of Mathematics Trends and Technology-IJMTT, 3(2), 54–62.
  3. Suliman, S.M.A. 2001. Pattern generating procedure for the cutting stock problem. International Journal of Production Economics, 74(1-3), 293–301.
  4. Suliman, S.M.A. 2006. A sequential heuristic procedure for the two-dimensional cutting-stock problem. International Journal of Production Economics, 99(1-2), 177–185.
  5. Salem, K.H., Silva, E., Oliveira, J.F., Carravilla, M.A. 2023. Mathematical models for the two-dimensional variable-sized cutting stock problem in the home textile industry. European Journal of Operational Research, 306(2), 549–566.
  6. Del Valle, A.M., De Queiroz, T.A., Miyazawa, F.K., Xavier, E.C. 2012. Heuristics for two-dimensional knapsack and cutting stock problems with items of irregular shape. Expert Systems with Applications, 39(16), 12589–12598.
  7. Xu, Y., Yang, G., Pan, C. 2013. A heuristic approach based on shape similarity for 2D irregular cutting stock problem. In: Li, K., Li, S., Li, D., Niu, Q. (Eds.), Intelligent Computing for Sustainable Energy and Environment, ICSEE 2012, Communications in Computer and Information Science, vol 355. Springer, Berlin, Heidelberg, 275–283.
  8. Beasley, J.E. 1985a. Algorithms for unconstrained two-dimensional guillotine cutting. Journal of the Operational Research Society, 36(4), 297–306.
  9. Christofides, N., Hadjiconstantinou, E. 1995. An exact algorithm for orthogonal 2-D cutting problems using guillotine cuts. European Journal of Operational Research, 83(1), 21–38.
  10. Beasley, J. E. 1985b. An exact two-dimensional non-guillotine cutting tree search procedure. Operations Research, 33(1), 49–64.
  11. Beasley, J. E. 2004. A population heuristic for constrained two-dimensional non-guillotine cutting. European Journal of Operational Research, 156(3), 601–627.
  12. Silva, E., Oliveira, J.F., Silveira, T., Mundim, L., Carravilla, M.A. 2023. The Floating-Cuts model: a general and flexible mixed-integer programming model for non-guillotine and guillotine rectangular cutting problems. Omega, 114, 102738.
  13. Parreño, F., Alonso, M.T., Alvarez-Valdés, R. 2020. Solving a large cutting problem in the glass manufacturing industry. European Journal of Operational Research, 287(1), 378–388.
  14. Cintra, G.F., Miyazawa, F.K., Wakabayashi, Y., Xavier, E.C. 2008. Algorithms for two-dimensional cutting stock and strip packing problems using dynamic programming and column generation. European Journal of Operational Research, 191(1), 61–85.
  15. Charnes, A., Cooper, W.W. 1977. Goal programming and multiple objective optimizations: Part 1. European Journal of Operational Research, 1(1), 39–54.
  16. Ignizio, J.P. 1983. An approach to the modeling and analysis of multiobjective generalized networks. European Journal of Operational Research, 12(4), 357–361.
  17. Morabito, R., Pureza, V. 2010. A heuristic approach based on dynamic programming and and/or-graph search for the constrained two-dimensional guillotine cutting problem. Annals of Operations Research, 179(1), 297-315.
  18. Cui, Y., Yang, L., Zhao, Z., Tang, T., Yin, M. 2013. Sequential grouping heuristic for the two-dimensional cutting stock problem with pattern reduction. International Journal of Production Economics, 144(2), 432-439.