*2.2.2 Application of the genetic algorithms*

Zhou and coworkers [22] used the genetic algorithm to schedule a dyeing process. Scheduling objectives are delivery time of the products, complete filling of the dyeing vessel, putting the same type, color, depth and production process of cloth in the same dyeing vessel, and sorting the depth of color such as from light to dark. Genetic algorithm was started with assigning all orders to different dye vats and producing a number of individuals. Each individual was checked to meet the delivery date. Then, fitness function was computed for the population. After the crossover and mutation steps, next generation was chosen according to the parent and child fitness. The procedure repeated itself until reaching the maximum number of evolutionary. The algorithm was verified on the data supplied from an enterprise in China. It was reported that the optimization of the schedule reduced the freshwater consumption about 18.4–21%, also resulting less wastewater.

The flowchart of a typical genetic algorithm is shown in **Figure 1**.

Jiang and coworkers applied genetic algorithm to the data obtained from a real process in China to optimize production schedule [23]. The aim of the study was to reduce the production time and the volume of freshwater consumed in the dyeing operation. The results showed that the optimized schedule could reduce the production time as 10–15% while reducing the volumes of freshwater and wastewater as 20–30 and 20%, respectively. Additionally, it was found that adding alternative production lines (from 1 to 4) in the process shortens the total production time and lessens the amount of wastewater.
