Genetic optimization of fabric utilization in apparel manufacturing

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.