Optimizing marker planning in apparel production using evolutionary strategies and neural networks

Wai Keung Wong, X. X. Wang, Z. X. Guo

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

1 Citation (Scopus)

Abstract

Marker planning in apparel production is a kind of packing problem in the research field of engineering. The irregular shapes of pattern pieces of a garment make the marker planning problem more complex. Few approaches have been developed to solve these problems, although effectiveness of packing determines industrial resource utilization. This study constructs a packing approach that integrates a grid approximation-based representation, a learning vector quantization neural network, a heuristic placement strategy and an integer representation-based(. μ+. λ) - evolutionary strategy to obtain efficient placement of irregular objects. Real data are used to demonstrate the performance of the proposed methodology. The results are compared with those obtained by a genetic algorithm-based packing approach and those generated from industrial practice, demonstrating the effectiveness of the proposed approach.
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.
Pages106-131
Number of pages26
ISBN (Print)9780857097798
DOIs
Publication statusPublished - 1 Jan 2013

Keywords

  • Evolutionary strategies
  • Irregular object packing
  • Neural network

ASJC Scopus subject areas

  • Engineering(all)

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