A strategy based on statistical modelling and multi-objective optimization to design a dishwasher cleaning cycle

Abstract

This study proposes a novel approach based on statistical learning and multi-objective optimization to reduce the need for experiments during the design phase of new cleaning cycles for household dishwashers. We first build regression models associated with the feature selection methods to predict the outputs of a dishwasher cleaning cycle by using the existing cleaning cycles’ program flows as input data and the results of the performance laboratory tests of the related cleaning cycles as output data. Then, a multi-objective optimization problem is defined by assigning the regression models and chosen features as objective functions and unknown decision variables, respectively. Obtained optimization problem is then solved by using evolutionary algorithms according to the designer’s preferences (or customers’ needs).