A product carbon footprint model for embodiment design based on macro-micro design features

Greenhouse gas emissions have become one of the most attractive global concerns for sustainable development. To reduce product life cycle carbon footprint, planning should begin at embodiment design phase. Accurate assessment of carbon footprint is the foundation of carbon footprint reduction. However, existing carbon footprint models are not applicable to embodiment design phase due to incomplete and limited design information. With this in mind, this paper proposes a carbon footprint model for embodiment design based on macro-micro design features. First, a Function-Structure-Feature (FSF) model for embodiment design is established to convey the design information. The concept of design features is introduced (as both macro and micro level). The macro design feature denotes the different operational states of the product and the constraint relationships between parts. The micro design feature denotes specific properties of parts. Then, a model of product carbon footprint based on design features is presented through analyzing the relationships between macro-micro design features and product carbon footprint. The feasibility of the proposed method is demonstrated by a gear hobbing machine. The amount of carbon footprint from each type of design feature can be predicted. Through the optimization of design features, product life cycle carbon footprint has decreased significantly.

Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint

In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed model manufacturing system, a product carbon footprint-oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system, is proposed to calculate the product carbon footprint in the mixed model manufacturing system. Nondominated sorting genetic algorithm-II (NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.