Optimizing apparel production order planning scheduling using genetic algorithms

Z. X. Guo, Wai Keung Wong, S. Y.S. Leung, J. T. Fan, S. F. Chan

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

1 Citation (Scopus)


In this chapter the order scheduling problem at the factory level is investigated. Various uncertainties are considered and described as random variables. A mathematical model for this order scheduling problem is presented with the objectives of maximizing the total satisfaction level of all orders and minimizing their total throughput time. Uncertain completion time and beginning time of production process are derived from probability theory. A genetic algorithm is developed to seek after the optimal order scheduling solution. Experiments are conducted to validate the proposed algorithm by using real-world production data. The experimental results show the effectiveness of the proposed algorithm.
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 pages26
ISBN (Print)9780857097798
Publication statusPublished - 1 Jan 2013


  • Genetic algorithms
  • Order scheduling
  • Probability theory
  • Uncertain processing time

ASJC Scopus subject areas

  • General Engineering


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