Multi-Objective Remanufacturing Processing Scheme Design and Optimization Considering Carbon Emissions

In the face of escalating environmental degradation and dwindling resources, the imperatives of prioritizing environmental protection, and conserving resources have come sharply into focus. Therefore, remanufacturing processing, as the core of remanufacturing, becomes a key step in solving the above problems. However, with the increasing number of failing products and the advent of Industry 5.0, there is a heightened request for remanufacturing in the context of environmental protection. In response to these shortcomings, this study introduces a novel remanufacturing process planning model to address these gaps. Firstly, the failure characteristics of the used parts are extracted by the fault tree method, and the failure characteristics matrix is established by the numerical coding method. This matrix includes both symmetry and asymmetry, thereby reflecting each attribute of each failure feature, and the remanufacturing process is expeditiously generated. Secondly, a multi-objective optimization model is devised, encompassing the factors of time, cost, energy consumption, and carbon emission. This model integrates considerations of failure patterns inherent in used parts and components, alongside the energy consumption and carbon emissions entailed in the remanufacturing process. To address this complex optimization model, an improved teaching–learning-based optimization (TLBO) algorithm is introduced. This algorithm amalgamates Pareto and elite retention strategies, complemented by local search techniques, bolstering its efficacy in addressing the complexities of the proposed model. Finally, the validity of the model is demonstrated by means of a single worm gear. The proposed algorithm is compared with NSGA-III, MPSO, and MOGWO to demonstrate the superiority of the algorithm in solving the proposed model.