Green and low carbon automated production has become a research hotspot. In this paper, the AGV transport resource constraint, machine layout and job setup time have been integrated into the background of a flexible job shop. From a whole life-cycle perspective, the AGV allocation strategy has been formulated by simulating multiple scenarios within the production system. Aimed at makespan, carbon footprint, and machine load, a green low-carbon flexible job shop scheduling model with multiple transport equipment (GFJSP-MT) has been constructed. To address this problem, a relevant case dataset was formed, and a heuristic strategy NSGA-II using a real number encoded embedded cycle to replace repeated individuals was proposed. Through longitudinal and horizontal comparisons, the effectiveness of the AGV allocation strategy has been verified and the optimum number of AGVs in the case determined. Finally the quality and diversity of the Pareto frontier solutions are compared and the scheduling scheme for each sub-objective are discussed. The results show that the model and algorithm constructed in this paper can effectively achieve the optimal scheduling of green flexible shop production.

To align with the adoption of electric vehicles in the transportation sector, this paper proposes the use of electric heavy-duty trucks for the logistics and distribution of large prefabricated building components. This approach aims to address the problems of high total costs and significant energy waste in prefabricated component transportation. Focusing on the multi-to-multi distribution mode, a two-level optimization model is constructed. The upper-level model is responsible for the reasonable allocation of demand points. The lower-level model optimizes the selection of road network nodes and charging stations along the delivery routes. It also dynamically adjusts charging timing and volume according to the real-time power situation. To enhance solution performance, a two-level multi-objective evolutionary algorithm based on Pareto theory is designed. This algorithm simultaneously optimizes distribution costs while coordinating path planning and charging strategies. Comparative experiments across different cases show that, compared with traditional single-level and multi-stage models, the proposed algorithm improves both solution accuracy and quality. Additionally, when compared with the scheduling scheme based on the full-charge capacity strategy, the dynamic charging strategy proposed in this paper reduces the total distribution cost by approximately 15.83%. These findings demonstrate that the constructed model and algorithm can effectively optimize the logistics and distribution of prefabricated components. They also provide a feasible solution for the practical application of electric vehicles in engineering logistics.