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A sustainable model based on genetic algorithm for garment redesign process

Manoj Kumar Paras (National Institute of Fashion Technology, Kangra, India)
Lichuan Wang (Graduate School, Soochow University, Suzhou, China)
Rudrajeet Pal (Faculty of Textiles, Engineering and Business, University of Borås, Borås, Sweden) (Department of Industrial Engineering and Management, University of Gävle, Gävle, Sweden)
Daniel Ekwall (Faculty of Textiles, Engineering and Business, University of Borås, Borås, Sweden) (Supply Chain Management and Social Responsibility, Hanken School of Economics, Helsinki, Finland)

Journal of Fashion Marketing and Management

ISSN: 1361-2026

Article publication date: 30 December 2022

43

Abstract

Purpose

This study proposes a garment modularization model based on an interactive genetic algorithm. The suggested model consists of extraction and identification of parts and the determination and implementation of connections. Rules and corresponding mathematical equations have been formulated for the part's extractions from the discarded products and connections for the redesigned products.

Design/methodology/approach

Sustainability entices scholars and practitioners while referring to reducing waste to control environmental degradation. One of the ways to safeguard natural resources is to increase the reuse of old or discarded products. The current study focuses on the redesign process to improve the reuse of products.

Findings

The intelligent system proposed based on the modularization techniques is expected to simplify and quantify the redesign process. The model can further help in the minimization of wastage and environmental degradation.

Originality/value

Presently, manual decisions are taken by the designers based on their memory, experience and intuition to extract and join the parts.

Keywords

Citation

Paras, M.K., Wang, L., Pal, R. and Ekwall, D. (2022), "A sustainable model based on genetic algorithm for garment redesign process", Journal of Fashion Marketing and Management, Vol. ahead-of-print No. ahead-of-print, pp. 1-18. https://doi.org/10.1108/JFMM-04-2022-0096

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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