A GA methodology for the scheduling of yarn-dyed textile production

Hsi M. Hsu*, Yai Hsiung, Ying Z. Chen, Muh-Cherng Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

This paper presents a scheduling approach for yarn-dyed textile manufacturing. The scheduling problem is distinct in having four characteristics: multi-stage production, sequence-dependent setup times, hierarchical product structure, and group-delivery (a group of jobs pertaining to a particular customer order must be delivered together), which are seldom addressed as a whole in literature. The scheduling objective is to minimize the total tardiness of customer orders. The problem is formulated as a mixed integer programming (MIP) model, which is computationally extensive. To reduce the problem complexity, we decomposed the scheduling problem into a sequence of sub-problems. Each sub-problem is solved by a genetic algorithm (GA), and an iteration of solving the whole sequence of sub-problems is repeated until a satisfactory solution has been obtained. Numerical experiment results indicated that the proposed approach significantly outperforms the EDD (earliest due date) scheduling method-currently used in the yarn-dyed textile industry.

Original languageEnglish
Pages (from-to)12095-12103
Number of pages9
JournalExpert Systems with Applications
Volume36
Issue number10
DOIs
StatePublished - 1 Dec 2009

Keywords

  • Genetic algorithm
  • Group-delivery
  • Multi-stage
  • Scheduling
  • Sequence-dependent setup
  • Textile

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