Personalized Garment Customization With Multitasking and Distinct Learning Effects

In this paper, we address a novel personalized garment customization, where two typical characteristics are considered: (i) Multitasking. The staffs have the ability to speed up their service efficiency if two orders are similar to each other. (ii) Unique learning effects. The subsequently served order may have a processing time reduction due to the multitasking ability of designers. The considered problem can be seen as a parallel-machine scheduling problem with multitasking and learning effects. For the problem, a mixed integer programming model is formulated to minimize the total tardiness of customized orders. Note that this is the first model for the personalized customization background. Experimental results on 90 randomly generated instances show that the effectiveness and validity of the proposed solution method. Therefore, our studied problem and method can provide some decision supports for the garment compames.