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Genetic optimization of fabric utilization in apparel manufacturing

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  • Wong, W.K.
  • Leung, S.Y.S.

Abstract

In apparel manufacturing, cut order planning (COP) plays a significant role in managing the cost of materials as fabric usually occupies more than 50% of the total manufacturing cost. Following the details of retail orders in terms of quantity, size and colour, COP seeks to minimize the total manufacturing costs by developing feasible cutting order plans with respect to material, machine and labour. In this paper, a genetic optimized decision-making model using adaptive evolutionary strategies is proposed to assist the production management of the apparel industry in the decision-making process of COP in which a new encoding method with a shortened binary string is devised. Four sets of real production data were collected to validate the proposed decision support method. The experimental results demonstrate that the proposed method can reduce both the material costs and the production of additional garments while satisfying the time constraints set by the downstream sewing department. Although the total operation time used is longer than that using industrial practice, the great benefits obtained by less fabric cost and extra quantity of garments planned and produced largely outweigh the longer operation time required.

Suggested Citation

  • Wong, W.K. & Leung, S.Y.S., 2008. "Genetic optimization of fabric utilization in apparel manufacturing," International Journal of Production Economics, Elsevier, vol. 114(1), pages 376-387, July.
  • Handle: RePEc:eee:proeco:v:114:y:2008:i:1:p:376-387
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    References listed on IDEAS

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    1. Kaschel, J. & Teich, Tobias & Zacher, Bernd, 2002. "Real-time dynamic shop floor scheduling using Evolutionary Algorithms," International Journal of Production Economics, Elsevier, vol. 79(2), pages 113-120, September.
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    Cited by:

    1. Nascimento, Daniela B. & Neiva de Figueiredo, J. & Mayerle, S.F. & Nascimento, P.R. & Casali, R.M., 2010. "A state-space solution search method for apparel industry spreading and cutting," International Journal of Production Economics, Elsevier, vol. 128(1), pages 379-392, November.
    2. Miguel Santoro & Felipe Lemos, 2015. "Irregular packing: MILP model based on a polygonal enclosure," Annals of Operations Research, Springer, vol. 235(1), pages 693-707, December.
    3. Kemmoé Tchomté, Sylverin & Gourgand, Michel, 2009. "Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems," International Journal of Production Economics, Elsevier, vol. 121(1), pages 57-67, September.
    4. Yu, M.T. & Lin, T.Y. & Hung, C., 2009. "Active-set sequential quadratic programming method with compact neighbourhood algorithm for the multi-polygon mass production cutting-stock problem with rotatable polygons," International Journal of Production Economics, Elsevier, vol. 121(1), pages 148-161, September.

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