Optimization Study of Laundry Cleaning Based on K-means Clustering and Simulated Annealing Algorithm

Abstract

Laundry is something people do every day, clothing cleaning effect and cleaning time, the number of times of cleaning, the amount of clothing detergent, water consumption and power consumption and other factors are closely related, whether in the daily cleaning, or the organization's business process, the choice of cleaning methods and the control of the cleaning cost are crucial. In order to find the optimal cleaning method, as well as the best cleaning solution, this paper carries out a study on laundry cleaning optimization based on nonlinear programming. Firstly, a cleaning method optimization method based on dynamic programming is proposed to get the optimal solution about the number of washing times and the amount of water used in each washing, and then, a cleaning cost optimization method based on simulated annealing is proposed to get the optimal cleaning solution that saves cost and has excellent cleaning effect. In this paper, ten different detergents are used to conduct experimental analysis on 36 pieces of clothing containing eight pollutants.