Hybrid Heuristics for Marker Planning in the Apparel Industry


Given the diversity of styles and sizes in apparel, marker planning which aims to arrange and move all the pattern parts of garments onto a long thin paper before the cutting process is a very important process for the apparel industry. In order to decrease the wastage of fabric after the cutting process, the marker layout essentially needs to be as compact as possible. In this paper, hybrid heuristics are proposed to conduct and achieve an optimized marker layout and length. First, a moving heuristic is presented as a new packing method to arrange and move the patterns without overlapping; here, an initial marker is presented to calculate the length. This heuristic considers multiple rotated angles and flipping positions of the patterns in order to obtain more diverse arrangements. With different arrangements, there is a higher chance of achieving an optimized marker layout and length. To further improve the solution received from the moving heuristic, soft computing algorithms are taken into account, including the genetic algorithm (GA), simulated annealing (SA), and hybrid genetic algorithm-simulated annealing (HGASA) to achieve a shorter length in the marker layout. The best marker length can be obtained from HGASA, which can save almost 28% of the length. Special cases with specific combinations in rotated angles are considered so that the industry can make informed choices on the algorithms suitable to address the marker planning problem.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28


  1. 1.

    Taiwan Textile Federation (2017) 2017年臺灣紡織工業概況-紡拓會. Retrieved April 29, 2019, from https://www.textiles.org.tw/TTF/main/content/wHandMenuFile.ashx?file_id=1

  2. 2.

    Wong, W.K.; Guo, Z.X.; Leung, S.Y.S.: Optimizing cut order planning in apparel production using evolutionary strategies. Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI), 81-105 (2013a)

  3. 3.

    Lodi, A.; Martello, S.; Monaci, M.: Two-dimensional packing problems: a survey. Eur. J. Oper. Res. 141(2), 241–252 (2002)

    MathSciNet  Article  Google Scholar 

  4. 4.

    Wu, C.L.; Chau, K.W.: Prediction of rainfall time series using modular soft computing methods. Eng. Appl. Artif. Intell. 26(3), 997–1007 (2013)

    Article  Google Scholar 

  5. 5.

    Taormina, R.; Chau, K.: ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS. Eng. Appl. Artif. Intell. 45, 429–440 (2015)

    Article  Google Scholar 

  6. 6.

    Ardabili, S.F.; Najafi, B.; Shamshirband, S.; Bidgoli, B.M.; Deo, R.C.; Chau, K.W.: Computational intelligence approach for modeling hydrogen production: a review. Eng. Appl. Comput. Fluid Mech. 12(1), 438–458 (2018)

    Google Scholar 

  7. 7.

    Shamshirband, S.; Rabczuk, T.; Chau, K.W.: A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access 7(1), 164650–164666 (2019)

    Article  Google Scholar 

  8. 8.

    Banan, A.; Nasiri, A.; Taheri-Garavand, A.: Deep learning-based appearance features extraction for automated carp species identification. Aquacult. Eng. 89, 102053 (2020)

    Article  Google Scholar 

  9. 9.

    Fan, Y.J.; Xu, K.K.; Wu, H.; Zheng, Y.; Tao, B.: Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition. MLP and LSTM network, IEEE Access 8, 25111–25121 (2020)

    Google Scholar 

  10. 10.

    Jacobs-Blecha, C.; Ammons, J.C.; Schutte, A.; Smith, T.: Cut order planning for apparel manufacturing. IIE Trans. 30(1), 79–90 (2007)

    Google Scholar 

  11. 11.

    Shi, J.Y.; Feng, M.G.: Niche genetic algorithm for two dimensional irregular parts optimal layout. Chin. J. Eng. Des. 14(2), 170–174 (2007)

    MathSciNet  Google Scholar 

  12. 12.

    Bortfeldt, A.: A genetic algorithm for the two-dimensional strip packing problem with rectangular pieces. Eur. J. Oper. Res. 172(3), 814–837 (2006)

    MathSciNet  Article  Google Scholar 

  13. 13.

    Goldberg, D.E.; Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)

    Article  Google Scholar 

  14. 14.

    Lin, Y.K.; Yeh, C.T.; Huang, P.S.: A hybrid ant-tabu algorithm for solving a multistate flow network reliability maximization problem. Appl. Soft Comput. 13(8), 3529–3543 (2013)

    Article  Google Scholar 

  15. 15.

    Van Laarhoven, P.J.; Aarts, E.H.: Simulated annealing. In: Simulated annealing: theory and applications (pp. 7–15). Springer, Dordrecht (1987)

  16. 16.

    Jakobs, S.: On genetic algorithms for the packing of polygons. Eur. J. Oper. Res. 88(1), 165–181 (1996)

    Article  Google Scholar 

  17. 17.

    M’Hallah, R.; Bouziri, A.: Heuristics for the combined cut order planning two-dimensional layout problem in the apparel industry. Int. Trans. Oper. Res. 23(1–2), 321–353 (2016)

    MathSciNet  Article  Google Scholar 

  18. 18.

    Mahadevan, A.: Optimization in computer-aided pattern packing (marking, envelopes) (1984)

  19. 19.

    Wong, W.K.; Wang, X.X.; Guo, Z.X.: Optimizing marker planning in apparel production using evolutionary strategies and neural networks. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): form production to retail. Woodhead Publishing Series in Textiles, 106-131 (2013b)

  20. 20.

    Bennell, J.A.; Oliveira, J.F.: The geometry of nesting problems: a tutorial. Eur. J. Oper. Res. 184(2), 397–415 (2008)

    MathSciNet  Article  Google Scholar 

  21. 21.

    Huang, E.; Korf, R.E.: Optimal rectangle packing: an absolute placement approach. J. Artif. Intell. Res. 46, 47–87 (2013)

    MathSciNet  Article  Google Scholar 

  22. 22.

    Sha, O.P.; Kumar, R.: Nesting of two-dimensional irregular parts within an irregular boundary using genetic algorithm. J. Ship Prod. 16(4), 222–232 (2000)

    Article  Google Scholar 

  23. 23.

    Jain, S.; Gea, H.C.: Two-dimensional packing problems using genetic algorithms. Eng. Comput. 14(3), 206–213 (1998)

    Article  Google Scholar 

  24. 24.

    Chen, P.H.; Shahandashti, S.M.: Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom. Constr. 18(4), 434–443 (2009)

    Article  Google Scholar 

  25. 25.

    Shalaby, M.A.; Kashkoush, M.: A particle swarm optimization algorithm for a 2-D irregular strip packing problem. Am. J. Oper. Res. 3(02), 268 (2013)

    Google Scholar 

  26. 26.

    Dowsland, K.A.; Vaid, S.; Dowsland, W.B.: An algorithm for polygon placement using a bottom-left strategy. Eur. J. Oper. Res. 141(2), 371–381 (2002)

    MathSciNet  Article  Google Scholar 

  27. 27.

    Wäscher, G.; Haußner, H.; Schumann, H.: An improved typology of cutting and packing problems. Eur. J. Oper. Res. 183(3), 1109–1130 (2007)

    Article  Google Scholar 

  28. 28.

    Burke, E.K.; Hellier, R.S.; Kendall, G.; Whitwell, G.: Complete and robust no-fit polygon generation for the irregular stock cutting problem. Eur. J. Oper. Res. 179(1), 27–49 (2007)

    Article  Google Scholar 

  29. 29.

    Whitley, D.: A genetic algorithm tutorial. Statist. Comput. 4(2), 65–85 (1994)

    Article  Google Scholar 

  30. 30.

    Pinheiro, P.R.; Amaro Júnior, B.; Saraiva, R.D.: A random-key genetic algorithm for solving the nesting problem. Int. J. Comput. Integr. Manuf. 29(11), 1159–1165 (2016)

    Article  Google Scholar 

  31. 31.

    Mundim, L.R.; Andretta, M.; de Queiroz, T.A.: A biased random key genetic algorithm for open dimension nesting problems using no-fit raster. Expert Syst. Appl. 81, 358–371 (2017)

    Article  Google Scholar 

  32. 32.

    Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    MathSciNet  Article  Google Scholar 

  33. 33.

    Martins, T.C.; Tsuzuki, M.D.S.G.: Simulated annealing applied to the irregular rotational placement of shapes over containers with fixed dimensions. Expert Syst. Appl. 37(3), 1955–1972 (2010)

    Article  Google Scholar 

  34. 34.

    Gomes, A.M.; Oliveira, J.F.: Solving irregular strip packing problems by hybridizing simulated annealing and linear programming. Eur. J. Oper. Res. 171(3), 811–829 (2006)

    Article  Google Scholar 

  35. 35.

    Lin, F.T.; Kao, C.Y.; Hsu, C.C.: Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Trans. Syst Man Cybern. 23(6), 1752–1767 (1993)

    Article  Google Scholar 

  36. 36.

    Leung, T.W.; Chan, C.K.; Troutt, M.D.: Application of a mixed simulated annealing-genetic algorithm heuristic for the two-dimensional orthogonal packing problem. Eur. J. Oper. Res. 145(3), 530–542 (2003)

    MathSciNet  Article  Google Scholar 

  37. 37.

    Metropolis, N.; Rosenbluth, A.W.; Rosenbluth, M.N.; Teller, A.H.; Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)

    Article  Google Scholar 

  38. 38.

    Chen, P.; Fu, Z.; Lim, A.; Rodrigues, B.: (2003) Two-dimensional packing for irregular shaped objects. In 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the (pp. 10-pp). IEEE

Download references

Author information



Corresponding author

Correspondence to Yu-Chung Tsao.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tsao, YC., Hung, CH. & Vu, TL. Hybrid Heuristics for Marker Planning in the Apparel Industry. Arab J Sci Eng (2021). https://doi.org/10.1007/s13369-020-05210-1

Download citation


  • Heuristics
  • Marker planning
  • Genetic algorithm
  • Simulated annealing
  • Hybrid algorithm