A new hybrid modeling of sustainable supply chain planning with imprecise data in textile industry

R. Ghasemy Yaghin () and K. Khalajmehri
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R. Ghasemy Yaghin: Amirkabir University of Technology
K. Khalajmehri: Amirkabir University of Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 9, No 79, 22435-22473

Abstract: Abstract This study concurrently examines multi-stage production and sustainable procurement to develop an integrated strategy. It includes fabric procurement, various stages of garment manufacturing (such as cutting, sewing, and finishing), and the distribution of clothing items, all within the context of uncertain supply chain data. Three critical objective functions i.e., minimizing costs, maximizing sustainable performance of weaving units, and maximizing the number of created jobs are considered in the problem domain. Importantly, the weaving units are evaluated through the lens of sustainability by successful hybridization of the analytical hierarchical process and Taguchi loss function. Our paper presents a single mathematical formulation that can manage the trade-offs between different objectives of supply chain master planning in the textile industry, while incorporating sustainability aspects. Additionally, the approach is enhanced by employing a fuzzy multi-objective non-linear optimization model, which adds robustness to the formulation. Our numerical study indicates that there is a direct relationship between apparel demand and social performance of the apparel maker. To fulfill its social responsibility, the textile industry needs to increase the demand for apparel by using effective marketing strategies. We demonstrate the importance of uncertainty consideration in textile industry, as our results indicate that optimal performance is compromised by neglecting uncertainties.

Keywords: Responsible production; Sustainable performance of weaving units; AHP-Taguchi loss function; Uncertainty; Fuzzy optimization; Statistical modeling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10668-024-05103-5

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