Optimization of garment sizing and cutting order planning in the context of mass customization

In this paper, we present a mass customization (MC)-oriented garment production planning system using mathematical optimization methods to generate the most efficient size chart and cutting order plan. It is composed of two subsystems, i.e., the fit-oriented garment sizing system and the cost-oriented garment cutting-order-planning (COP) system. In the fit-oriented sizing system, additional sizes are generated based on classical standard sizes, where a genetic algorithm (GA) is used to find the global optimum within an acceptable computation time. The comprehensive fit (CF), an overall garment fit evaluation of the target population, is taken as the objective function of the GA. In the cost-oriented COP system, under the hypothesis that fabric-cutting markers vary greatly (regarding the marker length and the cutting length) with various size combinations, an expanded integer programming (IP) model is developed to generate a cutting order plan with the lowest overall cutting cost (including the costs of fabric, spreading operation, and cutting operation) for the proposed sizing system. This MC-oriented production planning system has been validated with the performance of personalization (fit) and economy (cutting cost) through a case study on a women’s basic straight skirt. The experimental results show that the proposed system enables a considerable improvement of custom-fit at the expense of a very limited amount of extra cutting cost. Nevertheless, the cutting cost can fluctuate with the increasing number of extra sizes rather than increase monotonically with it. This study illustrates that these optimization approaches which support the garment sizing and COP for MC can help to gain a high customer satisfaction in terms of the garment fit and the cutting cost. More precisely, a GA is capable of rapidly finding the globally optimal sizing scenario, and an IP is able to work out the corresponding best cutting order plan. Furthermore, these proposed approaches can ultimately facilitate the evolution of garment production from mass production (MP) to MC.