Achieving sustainability through improvement in the textile wet process using particle swarm optimization and the tabu search algorithm

Abstract

As far as cloth production is concerned, the dyeing process in textiles forms a bottleneck procedure, as this process consumes a great deal of time and high volumes of water per unit of fabric for processing, which causes depletion of groundwater levels at a high rate. Besides, textile effluents are discharged into rivers or wetlands without proper treatment in many cases. Untreated textile effluent can contaminate groundwater and water bodies, reduce dissolved oxygen in the water, and affect aquatic ecosystems, which may indirectly cause climate change. The waste generated in dyeing is mainly due to the cleaning process. Thus, the dyeing process needs to be improved and optimized to solve the problem and reduce delay. To take effective measures for future improvement, it is essential to develop a nature-inspired tracking system. The amount of emission and the performance can be improved by utilizing scheduling as a tool. In this view, the dyeing process is formulated as a bi-objective optimization model to reduce the tardiness cost and minimize the emission of wastewater during the cleaning process of the dyeing vat. The current problem is of a difficult nature; thus, multi-objective particle swarm optimization in collaboration with the tabu search algorithm has been used to attain good and nondominant results. The tabu search algorithm is used along with ejection chains to focus on the objectives of emission reduction and increase the number of desired solutions. It was found that the total processing time was reduced by 4–5 hours and water was reduced by 500 liters for dyeing 200 kg of yarn daily.