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

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.