节能减排视角下共享制造资源调度研究OACHSSCD

Shared manufacturing has attracted extensive attention for its role in improving production efficiency and optimizing resource allocation.However,most existing studies inadequately address energy consumption and carbon emissions during production processes.To promote the sustained development of shared manufacturing,this paper investigates resource scheduling and optimization problems from the perspective of energy conservation and emission reduction.A multi-objective production scheduling optimization model is developed with the respective goals of minimizing costs and production time,and minimizing energy consumption and carbon emissions.To solve the model,a hybrid optimization algorithm is proposed by integrating the non-dominated sorting genetic algorithm with particle swarm optimization.Using real-world data for empirical analysis,the results show that:(1)as production utility increases,carbon emissions and energy consumption rise,with an accelerating marginal growth rate;(2)comparative analysis based on different weight settings for production cost and maximum production time indicates that a higher weight assigned to production cost is associated with greater production utility,suggesting that platform operators should place greater emphasis on cost control;(3)comparisons between the proposed hybrid genetic algorithm incorporating particle swarm optimization and other algorithms demonstrate that the hybrid approach achieves faster convergence and yields higher-quality solutions,thereby validating the applicability of the model.The findings provide a basis for advancing energy conservation and emission reduction efforts and for promoting the sustained development of shared manufacturing.