IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i9p3013-d165615.html
   My bibliography  Save this article

A Sustainable Application Based on Grouping Genetic Algorithm for Modularized Redesign Model in Apparel Reverse Supply Chain

Author

Listed:
  • Manoj Kumar Paras

    (College of Textile and Clothing Engineering, Soochow University, Suzhou 215006, China
    Faculty of Textile, Leather and Industrial Management, “Gheorghe Asachi” Technical University of Iași, 700050 Iași, Romania
    Faculty of Textiles, Engineering and Business, University of Borås, Allégatan 1, 501 90 Borås, Sweden)

  • Lichuan Wang

    (College of Textile and Clothing Engineering, Soochow University, Suzhou 215006, China)

  • Yan Chen

    (College of Textile and Clothing Engineering, Soochow University, Suzhou 215006, China)

  • Antonela Curteza

    (Faculty of Textile, Leather and Industrial Management, “Gheorghe Asachi” Technical University of Iași, 700050 Iași, Romania)

  • Rudrajeet Pal

    (Faculty of Textiles, Engineering and Business, University of Borås, Allégatan 1, 501 90 Borås, Sweden)

  • Daniel Ekwall

    (Faculty of Textiles, Engineering and Business, University of Borås, Allégatan 1, 501 90 Borås, Sweden
    Supply Chain Management and Social Responsibility, Hanken School of Economics, Arkadiankatu 22, 00100 Helsinki, Finland)

Abstract

The scarcity of natural resources and the problem of pollution have initiated the need for extending the life and use of existing products. The concept of the reverse supply chain provides an opportunity to recover value from discarded products. The potential for recovery and the improvement of value in the reverse supply chain of apparel has been barely studied. In this research, a novel modularized redesign model is developed and applied to the garment redesign process. The concept of modularization is used to extract parts from the end-of-use or end-of-life of products. The extracted parts are reassembled or reconstructed with the help of a proposed group genetic algorithm by using domain and industry-specific knowledge. Design fitness is calculated to achieve the optimal redesign. Subsequently, the practical relevance of the model is investigated with the help of an industrial case in Sweden. The case study finding reveals that the proposed method and model to calculate the design fitness could simplify the redesign process. The design fitness calculation is illustrated with the example of a polo t-shirt. The redesigned system-based modularization is in accordance with the practical situations because of its flexibility and viability to formulate redesign decisions. The grouping genetic algorithm could enable fast redesign decisions for designers.

Suggested Citation

  • Manoj Kumar Paras & Lichuan Wang & Yan Chen & Antonela Curteza & Rudrajeet Pal & Daniel Ekwall, 2018. "A Sustainable Application Based on Grouping Genetic Algorithm for Modularized Redesign Model in Apparel Reverse Supply Chain," Sustainability, MDPI, vol. 10(9), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3013-:d:165615
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/9/3013/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/9/3013/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manoj Kumar Paras & Daniel Ekwall & Rudrajeet Pal & Antonela Curteza & Yan Chen & Lichuan Wang, 2018. "An Exploratory Study of Swedish Charities to Develop a Model for the Reuse-Based Clothing Value Chain," Sustainability, MDPI, vol. 10(4), pages 1-19, April.
    2. De Lit, P. & Falkenauer, E. & Delchambre, A., 2000. "Grouping genetic algorithms: an efficient method to solve the cell formation problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 257-271.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cai, Ya-Jun & Choi, Tsan-Ming, 2020. "A United Nations’ Sustainable Development Goals perspective for sustainable textile and apparel supply chain management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    2. Esther Oluwadamilola Olufemi Rotimi & Cheree Topple & John Hopkins, 2021. "Towards A Conceptual Framework of Sustainable Practices of Post-consumer Textile Waste at Garment End of Lifecycle: A Systematic Literature Review Approach," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    3. Shashi & Piera Centobelli & Roberto Cerchione & Amit Mittal, 2021. "Managing sustainability in luxury industry to pursue circular economy strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 30(1), pages 432-462, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alessio Franconi & Fabrizio Ceschin & David Peck, 2022. "Structuring Circular Objectives and Design Strategies for the Circular Economy: A Multi-Hierarchical Theoretical Framework," Sustainability, MDPI, vol. 14(15), pages 1-26, July.
    2. Chih-Chun Lai & Ching-Erh Chang, 2020. "Clothing Disposal Behavior of Taiwanese Consumers with Respect to Environmental Protection and Sustainability," Sustainability, MDPI, vol. 12(22), pages 1-14, November.
    3. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.
    4. Salcedo-Sanz, Sancho & Deo, Ravinesh C. & Cornejo-Bueno, Laura & Camacho-Gómez, Carlos & Ghimire, Sujan, 2018. "An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia," Applied Energy, Elsevier, vol. 209(C), pages 79-94.
    5. Vila Goncalves Filho, Eduardo & Jose Tiberti, Alexandre, 2006. "A group genetic algorithm for the machine cell formation problem," International Journal of Production Economics, Elsevier, vol. 102(1), pages 1-21, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3013-:d:165615. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.