Optimizing fabric spreading and cutting schedules in apparel production using genetic algorithms and fuzzy set theory

Wai Keung Wong, Chun Kit Kwong, Pik Yin Mok, W. H. Ip

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

4 Citations (Scopus)


Today's apparel industry must respond to an ever-changing fashion market. Just-in-time production is a must-go direction. The apparel industry generates more production orders, which are split into smaller jobs to provide customers with timely and customized fashion products. Production planning is even more challenging if the due times of production orders are fuzzy and resource competing. In this chapter, genetic algorithms and fuzzy set theory generate just-in-time fabric-cutting schedules in a dynamic and fuzzy environment. Real production data were collected to validate the proposed genetic optimization method. Results demonstrate that genetically optimized schedules improve the satisfaction of production departments and reduce costs.
Original languageEnglish
Title of host publicationOptimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)
Subtitle of host publicationFrom Production to Retail
PublisherElsevier Inc.
Number of pages21
ISBN (Print)9780857097798
Publication statusPublished - 1 Jan 2013


  • Apparel
  • Fabric cutting
  • Fuzzy set theory
  • Genetic algorithms
  • Parallel machine scheduling

ASJC Scopus subject areas

  • Engineering(all)

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