TY - GEN
T1 - Personalized Garment Customization With Multitasking and Distinct Learning Effects
AU - He, Junkai
AU - Chu, Feng
AU - Zheng, Feifeng
AU - Chu, Chengbin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - Customization
KW - job-related learning effects
KW - mixed integer programming
KW - multitasking
KW - parallel machine scheduling
UR - https://www.scopus.com/pages/publications/85078776657
U2 - 10.1109/IESM45758.2019.8948216
DO - 10.1109/IESM45758.2019.8948216
M3 - Conference contribution
AN - SCOPUS:85078776657
T3 - Proceedings of the 2019 International Conference on Industrial Engineering and Systems Management, IESM 2019
BT - Proceedings of the 2019 International Conference on Industrial Engineering and Systems Management, IESM 2019
A2 - Zheng, Feifeng
A2 - Chu, Feng
A2 - Liu, Ming
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Industrial Engineering and Systems Management, IESM 2019
Y2 - 25 September 2019 through 27 September 2019
ER -