Nonlinear optimization reveals effective washing configurations for 132 garments, highlighting improvements in cleaning efficiency and resource use.
An optimal washing strategy is developed in this study by considering the initial dirt level, detergent solubility, and clothing material. The study investigated these effects on laundry frequency, cleaning efficiency, and laundry cost. A nonlinear optimization model was constructed to quantify the relationship between multiple stain types, fabric materials, and the solubilization capacities of various detergents. Subsequently, a depth-first search (DFS) algorithm was applied to identify resource-efficient mixed washing schemes for 132 garments, each containing 8 types of stains, based on washing frequency, residual stain level, and detergent solubility. Experimental results showed that the optimal configuration required only two wash cycles achieved a dirt removal ratio of 0.97, and reached a detergent solubility index of 0.98. This approach promotes resource-efficient laundry by reducing water and energy consumption, contributing to more sustainable washing practices.
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Geng et al. (2025) studied this question.
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