Intelligent production planning for complex garment manufacturing

Pik Yin Mok, T. Y. Cheung, Wai Keung Wong, S. Y S Leung, J. T. Fan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

Apparel production is characterised by labour-intensive manual operations, frequent style changes, seasonal demand and shortening production lead times. With fierce competition worldwide, many manufacturers are switching their production from mass mode to lean mode to shorten their response time to changes. In a complex mixed mode production environment, it is very important to allocate job orders to suitable production lines so as to ensure the effective utilization of production resources and on-time completion of all job orders. In this paper, planning algorithms are proposed for automatic job allocations based on group technology and genetic algorithms. For genetic algorithms based intelligent planning algorithms, single-run and multiple-run genetic algorithms are suggested. Real production data are used to validate the proposed method. The proposed algorithms has been shown being able to substantially improve planning quality. These planning algorithms are currently used by apparel manufacturers in Hong Kong as part of their routine planning operations.
Original languageEnglish
Pages (from-to)133-145
Number of pages13
JournalJournal of Intelligent Manufacturing
Volume24
Issue number1
DOIs
Publication statusPublished - 1 Feb 2013

Keywords

  • Apparel production
  • Genetic algorithms
  • Group technology
  • Intelligent ERP
  • Production planning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Industrial and Manufacturing Engineering

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