Personalized Garment Customization With Multitasking and Distinct Learning Effects

Junkai He, Feng Chu, Feifeng Zheng, Chengbin Chu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2019 International Conference on Industrial Engineering and Systems Management, IESM 2019
EditorsFeifeng Zheng, Feng Chu, Ming Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728115665
DOIs
Publication statusPublished - 1 Sept 2019
Externally publishedYes
Event2019 International Conference on Industrial Engineering and Systems Management, IESM 2019 - Shanghai, China
Duration: 25 Sept 201927 Sept 2019

Publication series

NameProceedings of the 2019 International Conference on Industrial Engineering and Systems Management, IESM 2019

Conference

Conference2019 International Conference on Industrial Engineering and Systems Management, IESM 2019
Country/TerritoryChina
CityShanghai
Period25/09/1927/09/19

Keywords

  • Customization
  • job-related learning effects
  • mixed integer programming
  • multitasking
  • parallel machine scheduling

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