An Uncertain Optimization Model for Low-Carbon Product Family Design

Abstract

Global warming has attracted more and more people’s attention. Since products are one of the main sources of GHG emission, the firm is seeking appropriate methods to reduce GHG emission of the product. At present, product family design is widely adopted for meeting the various demand of customers. To reduce the GHG emission of products, some methods have been proposed for low-carbon product family design in recently years. In existing research, the related data of low-carbon product family design is given as crisp value. However, in a real environment, some design data can’t be assessed accurately. To this end, this paper proposes a uncertain optimization model for low-carbon product family design. In the model, the related uncertain data for low-carbon product family design is given as interval numbers. Based on the objective of profit and GHG emission, the model can simultaneously determine product family configuration, supplier selection and price strategies of product variants. In addition, the genetic algorithm is developed to solve the established model. Finally, a case study is performed to demonstrate the effectiveness of the proposed approach.