A reinforcement learning-assisted multi-objective evolutionary algorithm for generating green change plans of complex products

Abstract

Design change planning is an inevitable part of the product development process. Evolutionary algorithms (EAs) have been widely adopted to search for optimal change paths due to their strong global search capabilities. However, many existing approaches overlook key environmental factors like carbon emissions. Furthermore, EAs often struggle with premature convergence when solving complex design problems. This paper aims to develop an effective algorithm for green product design changes by incorporating carbon emission metrics and reinforcement learning techniques. Firstly, a constrained multi-objective optimization model for the green product change planning problem is built for the first time. Besides change cost and duration, a green indicator, i.e., carbon emissions, is introduced into the model, which can make obtained change plans more suitable for actual needs. Next, a multi-strategy self-switching multi-objective evolutionary algorithm assisted by reinforcement learning (R-MSMOEA) is developed to improve the performance of EA on solving the above model. Finally, the proposed model and algorithm are applied in the design change problem of a specific type of Skyworth TV, and experimental results verify their feasibility and effectiveness.